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Kitchenham, B.: Guidelines for performing Systematic Literature Reviews in software engineering. EBSE Technical Report EBSE-2007-01

© Kitchenham, 2007
Guidelines for performing Systematic Literature Reviews in
Software Engineering
Version 2.3
EBSE Technical Report
Software Engineering Group
School of Computer Science and Mathematics
Keele University
Keele, Staffs
Department of Computer Science
University of Durham
9 July, 2007
0. Document Control Section
0.1 Contents
T0.UT TUDocument Control SectionUT......................................................................................84Hi
1HTU0.1UT TUContentsUT ..........................................................................................................85Hi
2HTU0.2UT TUDocument Version ControlUT .......................................................................... 86Hiii
3HTU0.3UT TUDocument development teamUT ........................................................................87Hv
4HTU0.4UT TUExecutive SummaryUT ......................................................................................88Hvi
5HTU0.5UT TUGlossaryUT ........................................................................................................89Hvi
6HTU1.UT TUIntroductionUT............................................................................................................90H1
7HTU1.1UT TUSource Material used in the Construction of the GuidelinesUT .........................91H1
8HTU1.2UT TUThe Guideline Construction ProcessUT..............................................................92H2
9HTU1.3UT TUThe Structure of the GuidelinesUT .....................................................................93H2
10HTU1.4UT TUHow to Use the GuidelinesUT ............................................................................94H2
11HTU2.UT TUSystematic Literature ReviewsUT ..............................................................................95H3
12HTU2.1UT TUReasons for Performing Systematic Literature ReviewsUT ...............................96H3
13HTU2.2UT TUThe Importance of Systematic Literature ReviewsUT........................................97H3
14HTU2.3UT TUAdvantages and disadvantagesUT ......................................................................98H4
15HTU2.4UT TUFeatures of Systematic Literature ReviewsUT....................................................99H4
16HTU2.5UT TUOther Types of ReviewUT ..................................................................................100H4
17HTU2.5.1UT TUSystematic Mapping StudiesUT..................................................................101H4
18HTU2.5.2UT TUTertiary ReviewsUT ....................................................................................102H5
19HTU3.UT TUEvidence Based Software Engineering in ContextUT ................................................103H5
20HTU4.UT TUThe Review ProcessUT ...............................................................................................104H6
21HTU5.UT TUPlanningUT .................................................................................................................105H7
22HTU5.1UT TUThe need for a systematic reviewUT...................................................................106H7
23HTU5.2 UT TUCommissioning a ReviewUT ..............................................................................107H8
24HTU5.3UT TUThe Research Question(s)UT ..............................................................................108H9
25HTU5.3.1UT TUQuestion TypesUT ......................................................................................109H9
26HTU5.3.2UT TUQuestion StructureUT ...............................................................................110H10
27HTU5.4UT TUDeveloping a Review ProtocolUT ....................................................................111H12
28HTU5.5UT TUEvaluating a Review ProtocolUT......................................................................112H13
29HTU5.6UT TULessons learned for protocol constructionUT ...................................................113H14
30HTU6.UT TUConducting the reviewUT .........................................................................................114H14
31HTU6.1UT TUIdentification of ResearchUT ............................................................................115H14
32HTU6.1.1UT TUGenerating a search strategyUT ................................................................116H14
33HTU6.1.2UT TUPublication BiasUT ...................................................................................117H15
34HTU6.1.3UT TUBibliography Management and Document RetrievalUT ..........................118H16
35HTU6.1.4UT TUDocumenting the SearchUT ......................................................................119H16
36HTU6.1.5UT TULessons learned for Search ProceduresUT................................................120H17
37HTU6.2UT TUStudy SelectionUT ............................................................................................121H18
38HTU6.2.1UT TUStudy selection criteriaUT.........................................................................122H18
39HTU6.2.2UT TUStudy selection processUT ........................................................................123H19
40HTU6.2.3UT TUReliability of inclusion decisionsUT.........................................................124H20
41HTU6.3UT TUStudy Quality AssessmentUT ...........................................................................125H20
42HTU6.3.1UT TUThe Hierarchy of EvidenceUT ..................................................................126H21
43HTU6.3.2UT TUDevelopment of Quality InstrumentsUT...................................................127H22
44HTU6.3.3UT TUUsing the Quality InstrumentUT...............................................................128H28
45HTU6.3.4UT TULimitations of Quality AssessmentUT ......................................................129H29
46HTU6.4UT TUData ExtractionUT ............................................................................................130H29
47HTU6.4.1UT TUDesign of Data Extraction FormsUT ........................................................131H29
48HTU6.4.2UT TUContents of Data Collection FormsUT......................................................132H30
49HTUCross-company modelUT .................................................................................133H31
50HTUWithin-company modelUT .................................................................................134H31
51HTUWhat measure was used to check the statistical significance of
prediction accuracy (e.g. absolute residuals, MREs)?UT ...........................135H32
52HTUWhat statistical tests were used to compare the results?UT.......................136H32
53HTUWhat were the results of the tests?UT ............................................................137H32
54HTUData SummaryUT ...............................................................................................138H32
55HTU6.4.3UT TUData extraction proceduresUT ..................................................................139H33
56HTU6.4.4UT TUMultiple publications of the same dataUT ................................................140H33
57HTU6.4.5UT TUUnpublished data, missing data and data requiring manipulationUT .......141H34
58HTU6.4.6UT TULessons learned about Data ExtractionUT................................................142H34
59HTU6.5UT TUData SynthesisUT..............................................................................................143H34
60HTU6.5.1UT TUDescriptive (Narrative) synthesisUT.........................................................144H34
61HTU6.5.2UT TUQuantitative SynthesisUT .........................................................................145H35
62HTU6.5.3UT TUPresentation of Quantitative ResultsUT ....................................................146H36
63HTU6.5.4UT TUQualitative SynthesisUT ...........................................................................147H37
64HTU6.5.5UT TUSynthesis of qualitative and quantitative studiesUT .................................148H38
65HTU6.5.6UT TUSensitivity analysisUT...............................................................................149H38
66HTU6.5.7UT TUPublication biasUT ....................................................................................150H39
67HTU6.5.8UT TULessons Learned about Data SynthesisUT................................................151H39
68HTU7.UT TUReporting the review (Dissemination)UT .................................................................152H39
69HTU7.1UT TUSpecifying the Dissemination StrategyUT ........................................................153H39
70HTU7.2UT TUFormatting the Main Systematic Review ReportUT.........................................154H40
71HTU7.3UT TUEvaluating Systematic Review ReportsUT .......................................................155H40
72HTU7.4UT TULessons Learned about Reporting Systematic Literature ReviewsUT..............156H40
73HTU8UT TUSystematic Mapping StudiesUT................................................................................157H44
74HTU9UT TUFinal remarksUT .......................................................................................................158H44
75HTU10UT TUReferencesUT........................................................................................................159H45
76HTUAppendix 1UT TUSteps in a systematic reviewUT ................................................................160H48
77HTUAppendix 2UT TUSoftware Engineering Systematic Literature ReviewsUT ........................161H50
78HTUAppendix 3 Protocol for a Tertiary study of Systematic Literature Reviews and
Evidence-based Guidelines in IT and Software EngineeringUT ......................................162H53
0.2 Document Version Control
status Version
Number Date Changes from previous version
Draft 0.1 1 April 2004 None
Published 1.0 29 June 2004 Correction of typos
Additional discussion of
problems of assessing evidence
Section 7 “Final Remarks”
Revision 1.1 17 August
2005 Corrections of typos.
Revision 1.9 25 October
2005 Changed title, added SMC as a
reviser, several new sections
added, finalisation of major
revisions should be version 2.0
Changes summarised below:
Added Section 2 – to but EBSE
in Context
Expanded the reporting of the
review processes
Added sections on Systematic
Mapping and Tertiary reviews
in section 4
Updated the Reporting the
Review Section
Added two final sections,
Systematic Mapping Studies
and Tertiary Reviews
Further major
revisions 2.0 17 March 2007 Revised the section on hierarchy
of studies to be consistent with
social science viewpoints.
Removed some general
discussion that was not well-
focused on the construction of
the guidelines.
Revised the section on quality
Added lessons learnt from SE
Removed final section on
Tertiary reviews (seemed
after internal
2.1 27 March 2007 Correction of Typos
Inclusion of a Glossary
Inclusion of guidelines
review construction process
Minor restructuring – Mapping
reviews and tertiary reviews
moved into section 3 to avoid
interfering with the flow of the
Further minor
revisions 2.2 4 April 2007 Typos and grammatical
A paragraph on how to read the
guidelines included in the
after external
2.3 20 July Amendments after external
review including the
introduction of more examples.
0.3 Document development team
This document was revised by members of the Evidence-Based Software
Engineering(EBSE) Project (EP/CS51839/X) which was funded by the UK
Economics and Physical Sciences Research Council.
Name Affiliation Role
Barbara Kitchenham Keele University, UK Lead author
Stuart Charters Lincoln University, NZ Second author
David Budgen University of Durham,
UK EBSE Internal
Pearl Brereton Keele University, UK EBSE Internal
Mark Turner Keele University, UK EBSE Internal
Steve Linkman Keele University, UK EBSE Internal
Magne Jørgensen Simula Research
Laboratory, Norway External reviewer
Emilia Mendes University of
Auckland, New
External reviewer
Giuseppe Visaggio University of Bari,
Italy External reviewer
0.4 Executive Summary
The objective of this report is to propose comprehensive guidelines for systematic
literature reviews appropriate for software engineering researchers, including PhD
students. A systematic literature review is a means of evaluating and interpreting all
available research relevant to a particular research question, topic area, or
phenomenon of interest. Systematic reviews aim to present a fair evaluation of a
research topic by using a trustworthy, rigorous, and auditable methodology.
The guidelines presented in this report were derived from three existing guidelines
used by medical researchers, two books produced by researchers with social science
backgrounds and discussions with researchers from other disciplines who are involved
in evidence-based practice. The guidelines have been adapted to reflect the specific
problems of software engineering research.
The guidelines cover three phases of a systematic literature review: planning the
review, conducting the review and reporting the review. They provide a relatively
high level description. They do not consider the impact of the research questions on
the review procedures, nor do they specify in detail the mechanisms needed to
perform meta-analysis.
0.5 Glossary
Meta-analysis. A form of secondary study where research synthesis is based on
quantitative statistical methods.
Primary study. (In the context of evidence) An empirical study investigating a
specific research question.
Secondary study. A study that reviews all the primary studies relating to a specific
research question with the aim of integrating/synthesising evidence related to a
specific research question.
Sensitivity analysis. An analysis procedure aimed at assessing whether the results of a
systematic literature review or a meta-analysis are unduly influenced by a small
number of studies. Sensitivity analysis methods involve assessing the impact of high
leverage studies (e.g. large studies or studies with atypical results), and ensuring that
overall results of a systematic literature remain the same if low quality studies (or
high quality) studies are omitted from the analysis, or analysed separately.
Systematic literature review (also referred to as a systematic review). A form of
secondary study that uses a well-defined methodology to identify, analyse and
interpret all available evidence related to a specific research question in a way that is
unbiased and (to a degree) repeatable.
Systematic review protocol. A plan that describes the conduct of a proposed
systematic literature review.
Systematic mapping study (also referred to as a scoping study). A broad review of
primary studies in a specific topic area that aims to identify what evidence is available
on the topic.
Tertiary study (also called a tertiary review). A review of secondary studies related to
the same research question.
1. Introduction
This document presents general guidelines for undertaking systematic reviews. The
goal of this document is to introduce the methodology for performing rigorous
reviews of current empirical evidence to the software engineering community. It is
aimed primarily at software engineering researchers including PhD students. It does
not cover details of meta-analysis (a statistical procedure for synthesising quantitative
results from different studies), nor does it discuss the implications that different types
of systematic review questions have on research procedures.
The original impetus for employing systematic literature review practice was to
support evidence-based medicine, and many guidelines reflect this viewpoint. This
document attempts to construct guidelines for performing systematic literature
reviews that are appropriate to the needs of software engineering researchers. It
discusses a number of issues where software engineering research differs from
medical research. In particular, software engineering research has relatively little
empirical research compared with the medical domain; research methods used by
software engineers are not as generally rigorous as those used by medical researchers;
and much empirical data in software engineering is proprietary.
1.1 Source Material used in the Construction of the Guidelines
The document is based on a review of three existing guidelines for systematic
reviews, the experiences of the Keele University and University of Durham Evidence-
based Software Engineering project, meetings with domain experts in a variety of
disciplines interested in evidence-based practice, and text books describing systematic
review principles:
The Cochrane Reviewer’s Handbook 163H[7] and Glossary 164H[8].
Guidelines prepared by the Australian National Health and Medical Research
Council 165H[1] and 166H[2].
Centre for Reviews and Dissemination (CRD) Guidelines for those carrying out or
commissioning reviews 167H[19].
Systematic reviews in the Social Sciences: A Practical Guide, Mark Petticrew and
Helen Roberts 168H[25]
Conducting Research Literature Reviews. From the Internet to Paper, 2P
P Edition,
Arlene Fink 169H[11].
Various articles and texts describing procedures for literature reviews in medicine
and social sciences (170H[20], 171H[13], and 172H[24]).
Meetings with various domain experts and centres including, the Evidence for
Policy and Practice Information and Coordinating Centre (EPPI Centre Social Science Research Unit Institute of Education,
University of London; CRD York University, Mark Petticrew, Glasgow
University; Andrew Booth, Sheffield University
Experiences from the Evidence Based Software Engineering Project at Keele
University and Durham University.
In particular, this document owes much to the CRD Guidelines.
1.2 The Guideline Construction Process
The construction process used for the guidelines was:
The guidelines were originally produced by a single person (Kitchenham).
They were then updated by two people (Charters and Kitchenham).
They were reviewed by members of the Evidence-based Software Engineering
project (Brereton, Budgen, Linkman, and Turner).
After correction, the guidelines were then circulated to external experts for
independent review.
The guidelines were further amended after the review by the external experts.
1.3 The Structure of the Guidelines
The structure of the guidelines is as follows:
Section 2 provides an introduction to systematic reviews.
Section 3 explains why social science SLR methodology is appropriate in the
context of software engineering research.
Section 4 specifies the stages in a systematic review.
Section 5 discusses the planning stages of a systematic review.
Section 6 discusses the stages involved in conducting a systematic review.
Section 7 discusses reporting a systematic review.
Section 8 discusses systematic mapping studies.
Throughout the guidelines we have incorporated examples taken from two recently
published systematic literature reviews 173H[21] and 174H[17]. Kichenham et al. 175H[21] addressed
the issue of whether it was possible to use cross-company benchmarking datasets to
produce estimation models suitable for use in a commercial company. Jørgensen 176H[17]
investigated the use of expert judgement, formal models and combinations of the two
approaches when estimating software development effort. In addition, Appendix 2
provides a list published systematic literature reviews assessed as high quality by the
authors of this report. These SLRs were identified and assessed as part of a systematic
literature review of recent software engineering SLRs. The protocol for the review is
documented in Appendix 3.
1.4 How to Use the Guidelines
These guidelines are aimed at software engineering researchers, PhD students, and
practitioners who are new to the concept of performing systematic literature reviews.
Readers who are unsure about what a systematic literature review is should start by
reading Section 2.
Readers who understand the principles of a systematic literature review can skip to
Section 4 to get an overview of the systematic literature review process. They should
then concentrate on Sections 5, 6 and 7, which describe in detail how to perform each
review phase. Sections 3 and 8 provide ancillary information that can be omitted on
first reading.
Readers who have more experience in performing systematic reviews may find the list
of tasks in Section 4, the quality checklists in Tables 5 and 6 and the reporting
structure presented in Table 7 sufficient for their needs.
Readers with detailed methodological queries are unlikely to find answers in this
document. They may find some of the references useful.
2. Systematic Literature Reviews
A systematic literature review (often referred to as a systematic review) is a means of
identifying, evaluating and interpreting all available research relevant to a particular
research question, or topic area, or phenomenon of interest. Individual studies
contributing to a systematic review are called primary studies; a systematic review is
a form of secondary study.
2.1 Reasons for Performing Systematic Literature Reviews
There are many reasons for undertaking a systematic literature review. The most
common reasons are:
To summarise the existing evidence concerning a treatment or technology e.g. to
summarise the empirical evidence of the benefits and limitations of a specific
agile method.
To identify any gaps in current research in order to suggest areas for further
To provide a framework/background in order to appropriately position new
research activities.
However, systematic literature reviews can also be undertaken to examine the extent
to which empirical evidence supports/contradicts theoretical hypotheses, or even to
assist the generation of new hypotheses (see for example 177H[14]).
2.2 The Importance of Systematic Literature Reviews
Most research starts with a literature review of some sort. However, unless a literature
review is thorough and fair, it is of little scientific value. This is the main rationale for
undertaking systematic reviews. A systematic review synthesises existing work in a
manner that is fair and seen to be fair. For example, systematic reviews must be
undertaken in accordance with a predefined search strategy. The search strategy must
allow the completeness of the search to be assessed. In particular, researchers
performing a systematic review must make every effort to identify and report research
that does not support their preferred research hypothesis as well as identifying and
reporting research that supports it.
"Indeed, one of my major complaints about the computer field is that
whereas Newton could say, "If I have seen a little farther than
others, it is because I have stood on the shoulders of giants," I am
forced to say, "Today we stand on each other's feet." Perhaps the
central problem we face in all of computer science is how we are to
get to the situation where we build on top of the work of others rather
than redoing so much of it in a trivially different way. Science is
supposed to be cumulative, not almost endless duplication of the same
kind of things". Richard Hamming 1968 Turning Award Lecture
Systematic literature reviews in all disciplines allow us to stand on the shoulders of
giants and in computing, allow us to get off each others’ feet.
2.3 Advantages and disadvantages
The advantages of systematic literature reviews are that:
The well-defined methodology makes it less likely that the results of the
literature are biased, although it does not protect against publication bias in the
primary studies.
They can provide information about the effects of some phenomenon across a
wide range of settings and empirical methods. If studies give consistent results,
systematic reviews provide evidence that the phenomenon is robust and
transferable. If the studies give inconsistent results, sources of variation can be
In the case of quantitative studies, it is possible to combine data using meta-
analytic techniques. This increases the likelihood of detecting real effects that
individual smaller studies are unable to detect.
The major disadvantage of systematic literature reviews is that they require
considerably more effort than traditional literature reviews. In addition, increased
power for meta-analysis can also be a disadvantage, since it is possible to detect small
biases as well as true effects.
2.4 Features of Systematic Literature Reviews
Some of the features that differentiate a systematic review from a conventional expert
literature review are:
Systematic reviews start by defining a review protocol that specifies the research
question being addressed and the methods that will be used to perform the review.
Systematic reviews are based on a defined search strategy that aims to detect as
much of the relevant literature as possible.
Systematic reviews document their search strategy so that readers can assess their
rigour and the completeness and repeatability of the process (bearing in mind that
searches of digital libraries are almost impossible to replicate).
Systematic reviews require explicit inclusion and exclusion criteria to assess each
potential primary study.
Systematic reviews specify the information to be obtained from each primary
study including quality criteria by which to evaluate each primary study.
A systematic review is a prerequisite for quantitative meta-analysis.
2.5 Other Types of Review
There are two other types of review that complement systematic literature reviews:
systematic mapping studies and tertiary reviews.
2.5.1 Systematic Mapping Studies
If, during the initial examination of a domain prior to commissioning a systematic
review, it is discovered that very little evidence is likely to exist or that the topic is
very broad then a systematic mapping study may be a more appropriate exercise than
a systematic review.
A systematic mapping study allows the evidence in a domain to be plotted at a high
level of granularity. This allows for the identification of evidence clusters and
evidence deserts to direct the focus of future systematic reviews and to identify areas
for more primary studies to be conducted. An outline of the systematic mapping study
process highlighting the main differences from the standard systematic review process
can be found in Section 8.
2.5.2 Tertiary Reviews
In a domain where a number of systematic reviews exist already it may be possible to
conduct a tertiary review, which is a systematic review of systematic reviews, in order
to answer wider research questions. A tertiary review uses exactly the same
methodology as a standard systematic literature review. It is potentially less resource
intensive than conducting a new systematic review of primary studies but is
dependent on sufficient systematic reviews of a high quality being available. The
protocol presented in Appendix 3 is a protocol for a tertiary review.
3. Evidence Based Software Engineering in Context
It is important to understand the relationship of Software Engineering to other
domains with regard to the applicability of the Evidence Based paradigm. In doing so,
we can identify how procedures adopted from other disciplines (particularly
medicine) need to be adapted to suit software engineering research and practice.
Budgen et al. 178H[6] interviewed practitioners in a number of domains that use evidence
based approaches to research, and compared their research practices with those of
software engineering. 179HTable 1 shows the results of their assessment of the similarity
between software engineering research practices and those of other domains. It shows
that software engineering is much more similar to the Social Sciences than it is to
medicine. This similarity is due to experimental practices, subject types and blinding
procedures. Within Software Engineering it is difficult to conduct randomised
controlled trials or to undertake double blinding. In addition, human expertise and the
human subject all affect the outcome of experiments.
Table 1 Comparing Software Engineering experimental methodology with that
of other disciplines
Discipline Comparison with SE (1 is perfect agreement, 0 is
complete disagreement)
Nursing & Midwifery 0.83
Primary Care 0.33
Organic Chemistry 0.83
Empirical Psychology 0.66
Clinical Medicine 0.17
Education 0.83
These factors mean that software engineering is significantly different from the
traditional medical arena in which systematic reviews were first developed. For this
reason we have revised these guidelines to incorporate recent ideas from the area of
social science (180H[25], 181H[11]). In addition, the choice of references on which to base these
guidelines was informed by our discussions with researchers in these disciplines.
4. The Review Process
A systematic literature review involves several discrete activities. Existing guidelines
for systematic reviews have slightly different suggestions about the number and order
of activities (see Appendix 1). However, the medical guidelines and sociological text
books are broadly in agreement about the major stages in the process.
This document summarises the stages in a systematic review into three main phases:
Planning the Review, Conducting the Review, Reporting the Review.
The stages associated with planning the review are:
Identification of the need for a review (See Section 5.1).
Commissioning a review (See Section 5.2).
Specifying the research question(s) (See Section 5.3).
Developing a review protocol (See Section 5.4).
Evaluating the review protocol (See Section 5.5).
The stages associated with conducting the review are:
Identification of research (See Section 6.1).
Selection of primary studies (See Section 6.2).
Study quality assessment (See Section 6.3).
Data extraction and monitoring (See Section 6.4).
Data synthesis (See Section 6.5).
The stages associated with reporting the review are:
Specifying dissemination mechanisms (See Section 7.1).
Formatting the main report (See Section 7.2).
Evaluating the report (See Section 7.3).
We consider all the above stages to be mandatory except:
Commissioning a review which depends on whether or not the systematic
review is being done on a commercial basis.
Evaluating the review protocol (5.5) and Evaluating the report (7.3) which are
optional and depend on the quality assurance procedures decided by the
systematic review team (and any other stakeholders).
The stages listed above may appear to be sequential, but it is important to recognise
that many of the stages involve iteration. In particular, many activities are initiated
during the protocol development stage, and refined when the review proper takes
place. For example:
The selection of primary studies is governed by inclusion and exclusion criteria.
These criteria are initially specified when the protocol is drafted but may be
refined after quality criteria are defined.
Data extraction forms initially prepared during construction of the protocol will
be amended when quality criteria are agreed.
Data synthesis methods defined in the protocol may be amended once data has
been collected.
The systematic reviews road map prepared by the Systematic Reviews Group at
Berkeley demonstrates the iterative nature of the systematic review process very
clearly 182H[24].
5. Planning
Prior to undertaking a systematic review it is necessary to confirm the need for such a
review. In some circumstances systematic reviews are commissioned and in such
cases a commissioning document needs to be written. However, the most important
pre-review activities are defining the research questions(s) that the systematic review
will address and producing a review protocol (i.e. plan) defining the basic review
procedures. The review protocol should also be subject to an independent evaluation
process. This is particularly important for a commissioned review.
5.1 The need for a systematic review
The need for a systematic review arises from the requirement of researchers to
summarise all existing information about some phenomenon in a thorough and
unbiased manner. This may be in order to draw more general conclusions about some
phenomenon than is possible from individual studies, or may be undertaken as a
prelude to further research activities.
Kitchenham et al. 183H[21] argued that accurate cost estimation is important for the software
industry; that accurate cost estimation models rely on past project data; that many companies
cannot collect enough data to construct their own models. Thus, it is important to know
whether models developed from data repositories can be used to predict costs in a specific
company. They noted that a number of studies have addressed that issue but have come to
different conclusions. They concluded that it is necessary to determine whether, or under
what conditions, models derived from data repositories can support estimation in a specific
Jørgensen 184H[17] pointed out in spite of the fact that most software cost estimation research
concentrates on formal cost estimation models and that a large number of IT managers know
about tools that implement formal models, most industrial cost estimation is based on expert
judgement. He argued that researchers need to know whether software professionals are
simply irrational, or whether expert judgement is just as accurate as formal models or has
other advantages that make it more acceptable than formal models.
In both cases the authors had undertaken research in the topic area and had first hand
knowledge of the research issues.
Prior to undertaking a systematic review, researchers should ensure that a systematic
review is necessary. In particular, researchers should identify and review any existing
systematic reviews of the phenomenon of interest against appropriate evaluation
criteria. The CRD 185H[19] suggests the following checklist:
What are the review’s objectives?
What sources were searched to identify primary studies? Were there any
What were the inclusion/exclusion criteria and how were they applied?
What criteria were used to assess the quality of primary studies?
How were quality criteria applied?
How were the data extracted from the primary studies?
How were the data synthesised?
How were differences between studies investigated?
How were the data combined?
Was it reasonable to combine the studies?
Do the conclusions flow from the evidence?
The CRD Database of Abstracts of Reviews of Effects (DARE) criteria
(H79HTU are even simpler. They are
based on four questions:
1. Are the review’s inclusion and exclusion criteria described and appropriate?
2. Is the literature search likely to have covered all relevant studies?
3. Did the reviewers assess the quality/validity of the included studies?
4. Were the basic data/studies adequately described?
We applied the DARE criteria both to Kitchenham et al.’s study 186H[21] and to Jørgensen’s study
187H[17]. We gave Kitchenham et al.’s study a score of 4 and Jørgensen’s study a score of 3.5.
Other studies scored using the DARE criteria are listed in Appendix 2.
From a more general viewpoint, Greenlaugh 188H[12] suggests the following questions:
Can you find an important clinical question, which the review addressed?
(Clearly, in software engineering, this should be adapted to refer to an important
software engineering question.)
Was a thorough search done of the appropriate databases and were other
potentially important sources explored?
Was methodological quality assessed and the trials weighted accordingly?
How sensitive are the results to the way that the review has been done?
Have numerical results been interpreted with common sense and due regard to the
broader aspects of the problem?
5.2 Commissioning a Review
Sometimes an organisation requires information about a specific topic but does not
have the time or expertise to perform a systematic literature itself. In such cases it will
commission researchers to perform a systematic literature review of the topic. When
this occurs the organisation must produce a commissioning document specifying the
work required.
A commissioning document will contain or consider the following items (adapted
from the CRD guidelines [12])
Project Title
Review Questions
Advisory/Steering Group Membership (Researchers, Practitioners, Lay
members, Policy Makers etc)
Methods of the review
Project Timetable
Dissemination Strategy
Support Infrastructure
The commissioning document can be used both to solicit tenders from research
groups willing to undertake the review and to act as a steering document for the
advisory group to ensure the review remains focused and relevant in the context.
The commissioning phase of a systematic review is not required for a research team
undertaking a review for their own needs or for one being undertaken by a PhD
student. If the commissioning stage is not undertaken then the dissemination strategy
should be incorporated into the review protocol. As yet, there are no examples of
commissioned SLRs in the software engineering domain.
5.3 The Research Question(s)
Specifying the research questions is the most important part of any systematic review.
The review questions drive the entire systematic review methodology:
The search process must identify primary studies that address the research
The data extraction process must extract the data items needed to answer the
The data analysis process must synthesise the data in such a way that the
questions can be answered.
5.3.1 Question Types
The most important activity during planning is to formulate the research question(s).
The Australian NHMR Guidelines 189H[1] identify six types of health care questions that
can be addressed by systematic reviews:
1. Assessing the effect of intervention.
2. Assessing the frequency or rate of a condition or disease.
3. Determining the performance of a diagnostic test.
4. Identifying aetiology and risk factors.
5. Identifying whether a condition can be predicted.
6. Assessing the economic value of an intervention or procedure.
In software engineering, it is not clear what the equivalent of a diagnostic test would
be, but the other questions can be adapted to software engineering issues as follows:
Assessing the effect of a software engineering technology.
Assessing the frequency or rate of a project development factor such as the
adoption of a technology, or the frequency or rate of project success or failure.
Identifying cost and risk factors associated with a technology.
Identifying the impact of technologies on reliability, performance and cost
Cost benefit analysis of employing specific software development technologies or
software applications.
Medical guidelines often provide different guidelines and procedures for different
types of question. This document does not go to this level of detail.
The critical issue in any systematic review is to ask the right question. In this context,
the right question is usually one that:
Is meaningful and important to practitioners as well as researchers. For example,
researchers might be interested in whether a specific analysis technique leads to a
significantly more accurate estimate of remaining defects after design inspections.
However, a practitioner might want to know whether adopting a specific analysis
technique to predict remaining defects is more effective than expert opinion at
identifying design documents that require re-inspection.
Will lead either to changes in current software engineering practice or to
increased confidence in the value of current practice. For example, researchers
and practitioners would like to know under what conditions a project can safely
adopt agile technologies and under what conditions it should not.
Will identify discrepancies between commonly held beliefs and reality.
Nonetheless, there are systematic reviews that ask questions that are primarily of
interest to researchers. Such reviews ask questions that identify and/or scope future
research activities. For example, a systematic review in a PhD thesis should identify
the existing basis for the research student’s work and make it clear where the
proposed research fits into the current body of knowledge.
Kitchenham et al. 190H[21] had three research questions:
Question 1: What evidence is there that cross-company estimation models are not
significantly different from within-company estimation models for predicting effort for
software/Web projects?
Question 2: What characteristics of the study data sets and the data analysis methods used in
the study affect the outcome of within- and cross-company effort estimation accuracy
Question 3: Which experimental procedure is most appropriate for studies comparing within-
and cross-company estimation models?
Jørgensen 191H[17] had two research questions:
1. Should we expect more accurate effort estimates when applying expert judgment or
2. When should software development effort estimates be based on expert judgment,
when on models, and when on a combination of expert judgment and models?
In both cases, the authors were aware from previous research that results were mixed, so in
each case they added a question aimed at investigating the conditions under which different
results are obtained.
5.3.2 Question Structure
Medical guidelines recommend considering a question about the effectiveness of a
treatment from three viewpoints:
The population, i.e. the people affected by the intervention.
The interventions, which are usually a comparison between two or more
alternative treatments.
The outcomes, i.e. the clinical and economic factors that will be used to compare
the interventions.
More recently Petticrew and Roberts suggest using the PICOC (Population,
Intervention, Comparison, Outcome, Context) criteria to frame research questions
192H[25]. These criteria extend the original medical guidelines with:
Comparison: I.e. what is the intervention being compared with
Context: i.e. what is the context in which the intervention is delivered.
In addition, study designs appropriate to answering the review questions may be
identified and used to guide the selection of primary studies.
We discuss these criteria from the viewpoint of software engineering below.
In software engineering experiments, the populations might be any of the following:
A specific software engineering role e.g. testers, managers.
A category of software engineer, e.g. a novice or experienced engineer.
An application area e.g. IT systems, command and control systems.
An industry group such as Telecommunications companies, or Small IT
A question may refer to very specific population groups e.g. novice testers, or
experienced software architects working on IT systems. In medicine the populations
are defined in order to reduce the number of prospective primary studies. In software
engineering far fewer primary studies are undertaken, thus, we may need to avoid any
restriction on the population until we come to consider the practical implications of
the systematic review.
The intervention is the software methodology/tool/technology/procedure that
addresses a specific issue, for example, technologies to perform specific tasks such as
requirements specification, system testing, or software cost estimation.
This is the software engineering methodology/tool/technology/procedure with which
the intervention is being compared. When the comparison technology is the
conventional or commonly-used technology, it is often referred to as the “control”
treatment. The control situation must be adequately described. In particular “not using
the intervention” is inadequate as a description of the control treatment. Software
engineering techniques usually require training. If you compare people using a
technique with people not using a technique, the effect of the technique is confounded
with the effect of training. That is, any effect might be due to providing training not
the specific technique. This is a particular problem if the participants are students.
Outcomes should relate to factors of importance to practitioners such as improved
reliability, reduced production costs, and reduced time to market. All relevant
outcomes should be specified. For example, in some cases we require interventions
that improve some aspect of software production without affecting another e.g.
improved reliability with no increase in cost.
A particular problem for software engineering experiments is the widespread use of
surrogate measures for example, defects found during system testing as a surrogate
for quality, or coupling measures for design quality. Studies that use surrogate
measures may be misleading and conclusions based on such studies may be less
For Software Engineering, this is the context in which the comparison takes place
(e.g. academia or industry), the participants taking part in the study (e.g. practitioners,
academics, consultants, students), and the tasks being performed (e.g. small scale,
large scale). Many software experiments take place in academia using student
participants and small scale tasks. Such experiments are unlikely to be representative
of what might occur with practitioners working in industry. Some systematic reviews
might choose to exclude such experiments although in software engineering, these
may be the only type of studies available.
Experimental designs
In medical studies, researchers may be able to restrict systematic reviews to primary
studies of one particular type. For example, Cochrane reviews are usually restricted to
randomised controlled trials (RCTs). In other circumstances, the nature of the
question and the central issue being addressed may suggest that certain study designs
are more appropriate than others. However, this approach can only be taken in a
discipline where the large number of research papers is a major problem. In software
engineering, the paucity of primary studies is more likely to be the problem for
systematic reviews and we are more likely to need protocols for aggregating
information from studies of widely different types.
Kitchenham et al.193H[21] used the PICO criteria and defined the question elements as
TPopulation:T software or Web project.
TIntervention:T cross-company project effort estimation model.
Comparison: single-company project effort estimation model
Outcomes: prediction or estimate accuracy.
Jørgensen 194H[17] did not use a structured version of his research questions.
5.4 Developing a Review Protocol
A review protocol specifies the methods that will be used to undertake a specific
systematic review. A pre-defined protocol is necessary to reduce the possibility of
researcher bias. For example, without a protocol, it is possible that the selection of
individual studies or the analysis may be driven by researcher expectations. In
medicine, review protocols are usually submitted to peer review.
The components of a protocol include all the elements of the review plus some
additional planning information:
Background. The rationale for the survey.
The research questions that the review is intended to answer.
The strategy that will be used to search for primary studies including search terms
and resources to be searched. Resources include digital libraries, specific journals,
and conference proceedings. An initial mapping study can help determine an
appropriate strategy.
Study selection criteria. Study selection criteria are used to determine which
studies are included in, or excluded from, a systematic review. It is usually
helpful to pilot the selection criteria on a subset of primary studies.
Study selection procedures. The protocol should describe how the selection
criteria will be applied e.g. how many assessors will evaluate each prospective
primary study, and how disagreements among assessors will be resolved.
Study quality assessment checklists and procedures. The researchers should
develop quality checklists to assess the individual studies. The purpose of the
quality assessment will guide the development of checklists.
Data extraction strategy. This defines how the information required from each
primary study will be obtained. If the data require manipulation or assumptions
and inferences to be made, the protocol should specify an appropriate validation
Synthesis of the extracted data. This defines the synthesis strategy. This should
clarify whether or not a formal meta-analysis is intended and if so what
techniques will be used.
Dissemination strategy (if not already included in a commissioning document).
Project timetable. This should define the review schedule.
An example of protocol for a tertiary review is given in Appendix 3. This is a simple
survey, so the protocol is quite short. In our experience, protocols can be very long
documents. In this case, the protocol is short because the search process is relatively
limited and the data extraction and data analysis processes are relatively
5.5 Evaluating a Review Protocol
The protocol is a critical element of any systematic review. Researchers must agree a
procedure for evaluating the protocol. If appropriate funding is available, a group of
independent experts should be asked to review the protocol. The same experts can
later be asked to review the final report.
PhD students should present their protocol to their supervisors for review and
The basic SLR review questions discussed in Section 5.1 can be adapted to assist the
evaluation of a systematic review protocol. In addition, the internal consistency of the
protocol can be checked to confirm that:
The search strings are appropriately derived from the research questions.
The data to be extracted will properly address the research question(s).
The data analysis procedure is appropriate to answer the research questions.
5.6 Lessons learned for protocol construction
Brereton et al. 195H[5] identify a number of issues that researchers should anticipate during
protocol construction:
A pre-review mapping study may help in scoping research questions.
Expect to revise questions during protocol development, as understanding of the
problem increases.
All the systematic review team members need to take an active part in
developing the review protocol, so they understand how to perform the data
extraction process.
Piloting the research protocol is essential. It will find mistakes in the data
collection and aggregation procedures. It may also indicate the need to change
the methodology intended to address the research questions including amending
the data extraction forms and synthesis methods.
Staples and Niazi 196H[27] recommend limiting the scope of a systematic literature by
choosing clear and narrow research questions.
6. Conducting the review
Once the protocol has been agreed, the review proper can start. However, as noted
previously, researchers should expect to try out each of the steps described in this
section when they construct their research protocol.
6.1 Identification of Research
The aim of a systematic review is to find as many primary studies relating to the
research question as possible using an unbiased search strategy. The rigour of the
search process is one factor that distinguishes systematic reviews from traditional
6.1.1 Generating a search strategy
It is necessary to determine and follow a search strategy. This should be developed in
consultation with librarians or others with relevant experience. Search strategies are
usually iterative and benefit from:
Preliminary searches aimed at both identifying existing systematic reviews and
assessing the volume of potentially relevant studies.
Trial searches using various combinations of search terms derived from the
research question.
Checking trial research strings against lists of already known primary studies.
Consultations with experts in the field.
A general approach is to break down the question into individual facets i.e.
population, intervention, comparison, outcomes, context, study designs as discussed
in Section 5.3.2. Then draw up a list of synonyms, abbreviations, and alternative
spellings. Other terms can be obtained by considering subject headings used in
journals and data bases. Sophisticated search strings can then be constructed using
Boolean ANDs and ORs.
Initial searches for primary studies can be undertaken using digital libraries but this is
not sufficient for a full systematic review. Other sources of evidence must also be
searched (sometimes manually) including:
Reference lists from relevant primary studies and review articles
Journals (including company journals such as the IBM Journal of Research and
Development), grey literature (i.e. technical reports, work in progress) and
conference proceedings
Research registers
The Internet.
It is also important to identify specific researchers to approach directly for advice on
appropriate source material.
Medical researchers have developed pre-packaged search strategies. Software
engineering researchers need to develop and publish such strategies including
identification of relevant digital libraries.
A problem for software engineering SLRs is that there may be relatively few studies
on a particular topic. In such cases it may be a good idea to look for studies in related
disciplines for example, sociology for group working practices, and psychology for
notation design and/or problem solving approaches.
Jørgensen 197H[16] investigated when we can expect expert estimates to have acceptable
accuracy in comparison with formal models by reviewing relevant human judgement studies
(e.g. time estimation studies) and comparing their results with the results of software
engineering studies.
6.1.2 Publication Bias
Publication bias refers to the problem that positive results are more likely to be
published than negative results. The concept of positive or negative results sometimes
depends on the viewpoint of the researcher. (For example, evidence that full
mastectomies were not always required for breast cancer was actually an extremely
positive result for breast cancer sufferers.)
However, publication bias remains a problem particularly for formal experiments,
where failure to reject the null hypothesis is considered less interesting than an
experiment that is able to reject the null hypothesis. Publication bias is even more of a
problem when methods/techniques are sponsored by influential groups in the software
industry. For example, the US MoD is an extremely important and influential
organisation which sponsored the development of the Capability Maturity Model and
used its influence to encourage industry to adopt the CMM. In such circumstances
few companies would want to publish negative results and there is a strong incentive
to publish papers that support the new method/technique.
Publication bias can lead to systematic bias in systematic reviews unless special
efforts are made to address this problem. Many of the standard search strategies
identified above are used to address this issue including:
Scanning the grey literature
Scanning conference proceedings
Contacting experts and researchers working in the area and asking them if they
know of any unpublished results.
In addition, statistical analysis techniques can be used to identify the potential
significance of publication bias (see Section 6.5.7).
6.1.3 Bibliography Management and Document Retrieval
Bibliographic packages such as Reference Manager or Endnote may be useful for
managing the large number of references that can be obtained from a thorough
literature search.
Once reference lists have been finalised the full articles of potentially useful studies
will need to be obtained. A logging system is needed to make sure all relevant studies
are obtained.
6.1.4 Documenting the Search
The process of performing a systematic literature review must be transparent and
replicable (as far as possible):
The review must be documented in sufficient detail for readers to be able to
assess the thoroughness of the search.
The search should be documented as it occurs and changes noted and justified.
The unfiltered search results should be saved and retained for possible reanalysis.
Procedures for documenting the search process are given in 198HTable 2.
Table 2 Search process documentation
Data Source Documentation
Digital Library Name of database
Search strategy for the database
Date of search
Years covered by search
Journal Hand Searches Name of journal
Years searched
Any issues not searched
Conference proceedings Title of proceedings
Name of conference (if different)
Title translation (if necessary)
Journal name (if published as part of a journal)
Efforts to identify
unpublished studies Research groups and researchers contacted (Names and contact details)
Research web sites searched (Date and URL)
Other sources Date Searched/Contacted
Any specific conditions pertaining to the search
Researchers should specify their rationale for:
The digital libraries to be searched.
The journal and conference proceedings to be searched.
The use of electronic or manual searches or a combination of both. Although
most text books emphasise the use of electronic search procedures, they are
not usually sufficient by themselves, and some researchers strongly advocate
the use of manual searches (e.g. Jørgensen, 199H[18]).
6.1.5 Lessons learned for Search Procedures
Brereton et al. 200H[5] identify several issues that need to be addressed when specifying
electronic search procedures:
There are alternative search strategies that enable you to achieve different sorts
of search completion criteria. You must select and justify a search strategy that
is appropriate for your research question. For example, knowing the publication
date of the first article on a specific topic restricts the years that need to be
searched. Also, if you are going to restrict your search to specific journals and
conference proceedings this needs to be justified.
We need to search many different electronic sources; no single source finds all
the primary studies.
Current software engineering search engines are not designed to support
systematic literature reviews. Unlike medical researchers, software engineering
researchers need to perform resource-dependent searches.
In an attempt to perform an exhaustive search Brereton et al. 201H[5] identified seven
electronic sources of relevance to Software Engineers:
ACM Digital library:
Google scholar (
Citeseer library (
Inspec (
ScienceDirect (
EI Compendex (
However, it may also be necessary to consider SpringerLink to access journals such as
Empirical Software Engineering and Springer Conference Proceedings, or SCOPUS
(which claims to be the largest database of abstracts and citations).
Kitchenham et al. 202H[21] used their structured questions to construct search strings for use with
electronic databases. The identified synonyms and alternative spellings for each of the
question elements and linked them using the Boolean OR e.g.:
Population: software OR application OR product OR Web OR WWW OR Internet OR World-
Wide Web OR project OR development
Intervention: cross company OR cross organisation OR cross organization OR multiple-
organizational OR multiple-organisational model OR modeling OR modelling effort OR cost
OR resource estimation OR prediction OR assessment
Contrast: within-organisation OR within-organization OR within-organizational OR within-
organisational OR single company OR single organisation
Outcome: Accuracy OR Mean Magnitude Relative Error
The search strings were constructed by linking the four OR lists using the Boolean AND.
The search strings were used on 6 digital libraries:
El Compendex
Science Direct
Web of Science
ACM Digital library
The search strings needed to be adapted to suit the specific requirements of the difference
data bases. In addition, the researchers searched several individual journals (J) and
conference proceedings (C) sources:
Empirical Software Engineering (J)
Information and Software Technology (J)
Software Process Improvement and Practice (J)
Management Science (J)
International Software Metrics Symposium (C)
International Conference on Software Engineering (C)
Evaluation and Assessment in Software Engineering (manual search) (C)
These sources were chosen because they had published papers on the topic.
In addition, Kitchenham et al. checked the references of each relevant article and approached
researchers who published on the topic to ask whether they had published (or were in the
process of publishing) any other articles on the topic.
Jørgensen 203H[17] used an existing database of journal papers that he had identified for another
review (Jørgensen and Shepperd 204H[15]). Jørgensen and Shepperd manually searched all
volumes of over 100 journals for papers on software cost estimation. The journals were
identified by reading reference lists of cost estimation papers, searching the Internet, and the
researchers own experience. Individual papers were categorised and recorded in a publicly
available data base (\BESTwebUTH.
For conference papers, Jørgensen searched papers identified by the INSPEC database using
the following search string:
‘effort estimation’ OR ‘cost estimation’) AND ‘software development’.
He also contacted authors of the relevant papers and was made aware of another relevant
Kitchenham et al. used the procedure recommended by most guidelines for performing
systematic review. However, it resulted in extremely long search strings that needed to be
adapted to specific search engines. Jørgensen 205H[17] used a database previously constructed
for a wide survey of software cost estimation. This is an example of how valuable a mapping
study can be. He also used a fairly simple search string on the INSPEC database.
Kitchenham et al attempted to produce a search string that was very specific to their research
question but they still found a large number of false positives. In practice, a simpler search
string might have been just as effective.
It is important to note that neither study based its search process solely on searching digital
libraries. Both studies had very specific research questions and the researchers were aware
that the number of papers addressing the topic would be small. Thus, both studies tried hard
to undertake a comprehensive search.
6.2 Study Selection
Once the potentially relevant primary studies have been obtained, they need to be
assessed for their actual relevance.
6.2.1 Study selection criteria
Study selection criteria are intended to identify those primary studies that provide
direct evidence about the research question. In order to reduce the likelihood of bias,
selection criteria should be decided during the protocol definition, although they may
be refined during the search process.
Inclusion and exclusion criteria should be based on the research question. They
should be piloted to ensure that they can be reliably interpreted and that they classify
studies correctly.
Kitchenham et al. used the following inclusion criteria:
any study that compared predictions of cross-company models with within-
company models based on analysis of single company project data.
They used the following exclusion criteria:
studies where projects were only collected from a small number of different sources
(e.g. 2 or 3 companies),
studies where models derived from a within-company data set were compared with
predictions from a general cost estimation model.
Jørgensen 206H[17] included papers that compare judgment-based and model-based software
development effort estimation. He also excluded one relevant paper due to “incomplete
information about how the estimates were derived”.
Medical standards make a point that it is important to avoid, as far as possible,
exclusions based on the language of the primary study. This may not be so
important for Software Engineering.
It is possible that inclusion decisions could be affected by knowledge of the
authors, institutions, journals or year of publication. Some medical researchers
have suggested reviews should be done after such information has been removed.
However, it takes time to do this and experimental evidence suggests that
masking the origin of primary studies does not improve reviews 207H[4].
6.2.2 Study selection process
Study selection is a multistage process. Initially, selection criteria should be
interpreted liberally, so that unless a study identified by the electronic and hand
searches can be clearly excluded based on title and abstract, a full copy should be
obtained. However, Brereton et al. 208H[5] point out thatThe standard of IT and software
engineering abstracts is too poor to rely on when selecting primary studies. You
should also review the conclusions.”
The next step is to apply inclusion/exclusion criteria based on practical issues 209H[11]
such as:
Participants or subjects
Research Design
Sampling method
Date of publication.
Staples and Niazi point out that it is sometimes necessary to consider the questions
that are not being addressed in order to refine your exclusion criteria 210H[27].
Staples and Niazi’s research question was
Why do organizations embark on CMM-based SPI initiatives?
They also defined complementary research questions that were not being investigated:
What motivates individuals to support the adoption of CMM-based SPI in an
Why should organizations embark on CMM-based SPI initiatives?
What reasons for embarking on CMM-based SPI are the most important to
What benefits have organizations received from CMM-based SPI initiatives?
How do organizations decide to embark on CMM-based SPI initiatives?
What problems do organizations have at the time that they decide to adopt CMM-based
This clarified the boundaries of their research question of interest for example they were
concerned with the motivations of organisations not the motivations of individuals and they
were concerned with why organisations rejected CMM not why the adopted it. They found that
this process directly improved and clarified their primary study selection and data extraction
Sometimes, researchers undertake a third stage in the selection process based on
detailed quality criteria.
Most general SLR text books recommend maintaining a list of excluded studies
identifying the reason for exclusion. However, in our experience, initial electronic
searches results in large numbers of totally irrelevant papers, i.e. papers that not only
do not address any aspect of the research questions but do not even have anything do
with software engineering. We, therefore, recommend maintaining a list of excluded
papers, only after the totally irrelevant papers have been excluded, in particular,
maintaining a record of those candidate primary studies that are excluded as a result
of the more detailed inclusion/exclusion criteria.
6.2.3 Reliability of inclusion decisions
When two or more researchers assess each paper, agreement between researchers can
be measured using the Cohen Kappa statistic 211H[9]. The initial value of the Kappa
statistics should be documented in the final report. Each disagreement must be
discussed and resolved. This may be a matter of referring back to the protocol or may
involve writing to the authors for additional information. Uncertainty about the
inclusion/exclusion of some studies should be investigated by sensitivity analysis.
A single researcher (such as a PhD student) should consider discussing included and
excluded papers with their advisor, an expert panel or other researchers. Alternatively,
individual researchers can apply a test-retest approach, and re-evaluate a random
sample of the primary studies found after initial screening to check the consistency of
their inclusion/exclusion decisions.
6.3 Study Quality Assessment
In addition to general inclusion/exclusion criteria, it is considered critical to assess the
“quality” of primary studies:
To provide still more detailed inclusion/exclusion criteria.
To investigate whether quality differences provide an explanation for differences
in study results.
As a means of weighting the importance of individual studies when results are
being synthesised.
To guide the interpretation of findings and determine the strength of inferences.
To guide recommendations for further research.
An initial difficulty is that there is no agreed definition of study “quality”. However,
the CRD Guidelines 212H[19] and the Cochrane Reviewers’ Handbook 213H[7] both suggest
that quality relates to the extent to which the study minimises bias and maximises
internal and external validity (see 214HTable 3).
Table 3 Quality concept definitions
Term Synonyms Definition
Bias Systematic error A tendency to produce results that depart systematically
from the ‘true’ results. Unbiased results are internally valid
Internal validity Validity The extent to which the design and conduct of the study are
likely to prevent systematic error. Internal validity is a
prerequisite for external validity.
External validity Generalisability,
Applicability The extent to which the effects observed in the study are
applicable outside of the study.
Most quality checklists (see Section 6.3.2) include questions aimed at assessing the
extent to which articles have addressed bias and validity.
6.3.1 The Hierarchy of Evidence
Medical guidelines suggest that an initial quality evaluation can be based on the type
of experiment design being used. Thus, we might rate a randomised controlled trial as
more trustworthy than an observational study. This has led to the concept of a
hierarchy of evidence with evidence from systematic reviews and randomised
controlled experiments at the top of the hierarchy and evidence from quasi-
experiments and expert opinion at the bottom of the hierarchy (see 215H[19] and 216H[2]).
Researchers can then use these hierarchies to restrict the type of studies they include
in their systematic literature review.
Recently, Petticrew and Roberts 217H[25] have suggested that this idea is too simplistic.
They point out that some types of design are better than others at addressing different
types of question. For example, qualitative studies are more appropriate than
randomised experiments for assessing whether practitioners find a new technology
appropriate for the type of applications they have to build. Thus, if we want to restrict
ourselves to studies of a specific type we should restrict ourselves to studies that are
best suited to addressing our specific research questions.
However, there is evidence that observational (e.g. correlation) studies can be
unreliable. Medical researchers have often discovered that the results of extremely
large scale observational studies have been overturned by the results of randomised
controlled trials. A recent example is that of the supposed benefits of vitamin C 218H[22].
Two large scale observational studies had previously suggested that taking vitamin C
protected against heart disease. Lawlor et al. 219H[22] suggest that the reason
observational studies found a result that could not be observed in randomised trials
was that use of vitamin C was a surrogate for other life-style characteristics that
protect against heart disease such as exercising and keeping to a healthy diet. This is
an issue that needs to be taken seriously in software engineering where much of our
research on topics such as software cost estimation and project success factors are
correlation studies. Good observational studies need to consider possible confounding
effects, put in place methods to measure them and adjust any analyses to allow for
their effect. In particular, they need to include sensitivity analysis to investigate the
impact of measured and unmeasured confounders.
6.3.2 Development of Quality Instruments
Detailed quality assessments are usually based on “quality instruments” which are
checklists of factors that need to be evaluated for each study. If quality items within a
checklist are assigned numerical scales, numerical assessments of quality can be
Checklists are usually derived from a consideration of factors that could bias study
results. The CRD Guidelines 220H[19], the Australian National Health and Medical
Research Council Guidelines 221H[1], and the Cochrane Reviewers’ Handbook 222H[7] all refer
to four types of bias shown in 223HTable 4. (We have amended the definitions (slightly)
and protection mechanisms (considerably) to address software engineering rather than
medicine.) In particular, medical researchers rely on “blinding” subjects and
experimenters (i.e. making sure that neither the subject nor the researcher knows
which treatment a subject is assigned to) to address performance and measurement
bias. However, that protocol is usually impossible for software engineering
Table 4 Types of Bias
Type Synonyms Definition Protection mechanism
bias Allocation
bias Systematic difference between
comparison groups with respect
to treatment
Randomisation of a large
number of subjects with
concealment of the allocation
method (e.g. allocation by
computer program not
experimenter choice).
bias Systematic difference is the
conduct of comparison groups
apart from the treatment being
Replication of the studies using
different experimenters.
Use of experimenters with no
personal interest in either
bias Detection
Bias Systematic difference between
the groups in how outcomes are
Blinding outcome assessors to
the treatments is sometimes
Attrition bias Exclusion
bias Systematic differences between
comparison groups in terms of
withdrawals or exclusions of
participants from the study
Reporting of the reasons for all
withdrawals. Sensitivity analysis
including all excluded
The factors identified in 224HTable 4 can be refined into a quality instrument by
Generic items that relate to features of particular study designs, such as survey
designs, experimental designs, and qualitative study designs.
Specific items that relate to the review’s subject area such as the particular
method of cross-validation used in a study of cost estimation prediction accuracy.
Checklists are also developed by considering bias and validity problems that can
occur at the different stages in an empirical study:
There are many published quality checklists for different types of empirical study.
The medical guidelines all provide checklists aimed at assisting the quality
assessment undertaken during a systematic literature review as do Fink 225H[11] and
Petticrew and Roberts 226H[25]. In addition, Crombie 227H[10] and Greenhalgh 228H[12] also
provide checklists aimed at assisting a reader to evaluate a specific article. Shaddish et
al. 229H[25] discuss quasi-experimental designs and provide an extensive summary of
validity issues affecting them. However, each source identifies a slightly different set
of questions and there is no standard agreed set of questions.
For quantitative studies we have accumulated a list of questions from 230H[10], 231H[11], 232H[12],
233H[19] and 234H[25] and organised them with respect to study stage and study type (see
235HTable 5). We do not suggest that anyone uses all the questions. Researchers should
adopt Fink’s suggestion 236H[11] which is to review the list of questions in the context of
their own study and select those quality evaluation questions that are most appropriate
for their specific research questions. They may need to construct a measurement scale
for each item since sometimes a simple Yes/No answer may be misleading. Whatever
form the quality instrument takes, it should be assessed for reliability and usability
during the trials of the study protocol before being applied to all the selected studies.
Kitchenham et al. 237H[21] constructed a quality questionnaire based on 5 issues affecting the
quality of the study which were scored to provide an overall measure of study quality:
1. Is the data analysis process appropriate?
1.1 Was the data investigated to identify outliers and to assess distributional properties
before analysis?
1.2 Was the result of the investigation used appropriately to transform the data and select
appropriate data points?
2. Did studies carry out a sensitivity or residual analysis?
2.1 Were the resulting estimation models subject to sensitivity or residual analysis?
2.2 Was the result of the sensitivity or residual analysis used to remove abnormal data
points if necessary?
3. Were accuracy statistics based on the raw data scale?
4. How good was the study comparison method?
4.1 Was the single company selected at random (not selected for convenience) from
several different companies?
4.2 Was the comparison based on an independent hold out sample (0.5) or random
subsets (0.33), leave-one-out (0.17), no hold out (0)? The scores used for this item reflect
the researchers opinion regarding the stringency of each criterion.
5. The size of the within-company data set, measured according to the criteria presented
below. Whenever a study used more than one within-company data set, the average score
was used:
Less than 10 projects: Poor quality (score = 0)
Between 10 and 20 projects: Fair quality (score = 0.33)
Between 21 and 40 projects: Good quality (score = 0.67)
More than 40 projects: Excellent quality (score = 1)
They also considered the reporting quality based on 4 questions:
1. Is it clear what projects were used to construct each model?
2. Is it clear how accuracy was measured?
3. Is it clear what cross-validation method was used?
4. Were all model construction methods fully defined (tools and methods used)?
It is good practice not to include quality of study and quality of reporting scores in a single
metric but Kitchenham et al. proposed using a weighted measure giving less weight to the
reporting quality score.
Kitchenham et al.’s quality questionnaire was based on the specific nature of the primary
studies (such as the method of cross-validation used) as well as more general quality issues
(such as sample size, and sensitivity analysis).
Jørgensen 238H[17] did not undertake a specific quality assessment of the primary studies.
Table 5 Summary Quality Checklist for Quantitative Studies
Question Quantitative Empirical
Studies (no specific type) Correlation
Surveys Experiments Source
Are the aims clearly stated? X X X X 239H[11], 240H[10]
Was the study designed with these questions in mind? X 241H[25]
Do the study measures allow the questions to be answered? X X 242H[10], 243H[25]
What population was being studied? X 244H[25]
Who was included? X 245H[12]
Who was excluded? X 246H[12]
How was the sample obtained (e.g. postal, interview, web-
based)? X 247H[10], 248H[12], 249H[25]
Is the survey method likely to have introduced significant
bias? X 250H[25]
Is the sample representative of the population to which the
results will generalise? X X 251H[10], 252H[25]
Were treatments randomly allocated? X 253H[10]
Is there a comparison or control group? X X X 254H[12]
If there is a control group, are participants similar to the
treatment group participants in terms of variables that may
affect study outcomes?
255H[10], 256H[12]
Was the sample size justified X X X 257H[10], 258H[12]
If the study involves assessment of a technology, is the
technology clearly defined? X X X X 259H[11]
Could the choice of subjects influence the size of the
treatment effect? X 260H[10], 261H[11],
Could lack of blinding introduce bias? X 264H[10]
Are the variables used in the study adequately measured
(i.e. are the variables likely to be valid and reliable)? X X X X 265H[10], 266H[11],
Are the measures used in the study fully defined? X X X X 269H[11]
Are the measures used in the study the most relevant ones
for answering the research questions? X X X X 270H[11], 271H[19],272H[25]
Is the scope (size and length) of the study sufficient to
allow for changes in the outcomes of interest to be
273H[19], 274H[12], 275H[25]
Did untoward events occur during the study? X X X X 276H[10]
Was outcome assessment blind to treatment group? X X 277H[19], 278H[12], 279H[25]
Are the data collection methods adequately described? X X X X 280H[11]
If two groups are being compared, were they treated
similarly within the study? X 281H[12], 282H[25]
If the study involves participants over time, what proportion
of people who enrolled at the beginning dropped out? X X X 283H[10], 284H[11]
How was the randomisation carried out? X 285H[10]
What was the response rate? X 286H[10], 287H[25]
Was the denominator (i.e. the population size) reported? X 288H[25]
Do the researchers explain the data types (continuous,
ordinal, categorical)? X X X X 289H[11]
Are the study participants or observational units adequately
described? For example, SE experience, type (student,
practitioner, consultant), nationality, task experience and
other relevant variables.
290H[12], 291H[25]
Were the basic data adequately described? X X X X 292H[10]
Have “drop outs” introduced bias? X X X 293H[11], 294H[12], 295H[25]
Are reasons given for refusal to participate? X X X 296H[11]
Are the statistical methods described? X X X X 297H[10], 298H[11], 299H[19]
Is the statistical program used to analyse the data
referenced? X X X X 300H[11]
Are the statistical methods justified? X X X X 301H[11]
Is the purpose of the analysis clear? X X X X 302H[11]
Are scoring systems described? X X 303H[11]
Are potential confounders adequately controlled for in the
analysis? X X X X 304H[11]
Do the numbers add up across different tables and X X X X 305H[10], 306H[11]
If different groups were different at the start of the study or
treated differently during the study, was any attempt made
to control for these differences, either statistically or by
307H[12], 308H[25]
If yes, was it successful? X X X 309H[25]
Was statistical significance assessed? X X X X 310H[10]
If statistical tests are used to determine differences, is the
actual p value given? X X X X 311H[11]
If the study is concerned with differences among groups,
are confidence limits given describing the magnitude of any
observed differences?
Is there evidence of multiple statistical testing or large
numbers of post hoc analysis? X X X X 313H[10], 314H[25]
How could selection bias arise? X X X 315H[10], 316H[25]
Were side-effects reported? 317H[10]
Are all study questions answered? X X X X 318H[11]
What do the main findings mean? X X X X 319H[10]
Are negative findings presented? X X X X 320H[11]
If statistical tests are used to determine differences, is
practical significance discussed? X X X X 321H[11]
If drop outs differ from participants, are limitations to the
results discussed? X X X 322H[11]
How are null findings interpreted? (I.e. has the possibility
that the sample size is too small been considered?) X X X X 323H[10], 324H[12]
Are important effects overlooked? X X X X 325H[10]
How do results compare with previous reports? X X X X 326H[10]
How do the results add to the literature? X X X X 327H[12]
What implications does the report have for practice? X X X X 328H[10]
Do the researchers explain the consequences of any
problems with the validity/reliability of their measures? X X X X 329H[11]
If a review includes qualitative studies, it will be necessary to assess their quality.
330HTable 6 provides a checklist of assessing the quality of qualitative studies.
Table 6 Checklist for qualitative studies
Number Question Source
1 How credible are the findings? 331H[12], 332H[25]
1.1 If credible, are they important? 333H[12]
2 How has knowledge or understanding been extended by the
334H[12], 335H[25]
3 How well does the evaluation address its original aims and
4 How well is the scope for drawing wider inference explained? 337H[25]
5 How clear is the basis of evaluative appraisal? 338H[25]
6 How defensible is the research design? 339H[12], 340H[25], 341H[11]
7 How well defined are the sample design/target selection of
342H[12], 343H[25], 344H[11]
8 How well is the eventual sample composition and coverage
9 How well was data collection carried out? 346H[12], 347H[25], 348H[11]
10 How well has the approach to, and formulation of, analysis
been conveyed?
349H[12], 350H[25], 351H[11]
11 How well are the contexts and data sources retained and
12 How well has diversity of perspective and context been
13 How well have detail, depth, and complexity (i.e. richness) of
the data been conveyed?
14 How clear are the links between data, interpretation and
conclusions – i.e. how well can the route to any conclusions
be seen?
15 How clear and coherent is the reporting? 356H[25]
16 How clear are the assumptions/theoretical perspectives/values
that have shaped the form and output of the evaluation?
357H[12], 358H[25], 359H[11]
17 What evidence is there of attention to ethical issues? 360H[25]
18 How adequately has the research process been documented? 361H[25]
6.3.3 Using the Quality Instrument
It is important that researchers not only define the quality instrument in the study
protocol but also specify how the quality data are to be used. Quality data can be used
in two rather different ways:
1. To assist primary study selection. In this case, the quality data are used to
construct detailed inclusion/exclusion criteria. The quality data must be collected
prior to the main data collection activity using separate data collection forms.
2. To assist data analysis and synthesis. In this case the quality data are used to
identify subsets of the primary study to investigate whether quality differences are
associated with different primary study outcomes. The quality data can be
collected at the same time as the main data extraction activity using a joint form.
It is of course possible to have both types of quality data in the same systematic
Kitchenham et al. 362H[21] used the quality score to investigate whether the results of the primary
study were associated with study quality. They also investigated whether some of the
individual quality factors (i.e. sample size, validation method) were associated with primary
study outcome.
Some researchers have suggested weighting meta-analysis results using quality
scores. This idea is not recommended by any of the medical guidelines.
If a systematic review includes studies of different types, it is necessary to use an
appropriate quality instrument for each study type. In some cases a common set of
quality evaluation questions may be suitable for all the quantitative studies included in
a systematic review, but if a review includes qualitative and quantitative studies
different checklists will be essential.
6.3.4 Limitations of Quality Assessment
Primary studies are often poorly reported, so it may not be possible to determine how
to assess a quality criterion. It is tempting to assume that because something wasn’t
reported, it wasn’t done. This assumption may be incorrect. Researchers should
attempt to obtain more information from the authors of the study. Petticrew and
Roberts 363H[25] explicitly point out that quality checklists need to address
methodological quality not reporting quality.
There is limited evidence of relationships between factors that are thought to affect
validity and actual study outcomes. Evidence suggests that inadequate concealment of
allocation and lack of double-blinding result in over-estimates of treatment effects,
but the impact of other quality factors is not supported by empirical evidence.
It is possible to identify inadequate or inappropriate statistical analysis, but without
access to the original data it is not possible to correct the analysis. Very often software
data is confidential and cannot therefore be made generally available to researchers.
In some cases, software engineers may refuse to make their data available to other
researchers because they want to continue publishing analyses of the data.
6.4 Data Extraction
The objective of this stage is to design data extraction forms to accurately record the
information researchers obtain from the primary studies. To reduce the opportunity
for bias, data extraction forms should be defined and piloted when the study protocol
is defined.
6.4.1 Design of Data Extraction Forms
The data extraction forms must be designed to collect all the information needed to
address the review questions and the study quality criteria. If the quality criteria are to
be used to identify inclusion/exclusion criteria, they require separate forms (since the
information must be collected prior to the main data extraction exercise). If the quality
criteria are to be used as part of the data analysis, the quality criteria and the review
data can be included in the same form.
In most cases, data extraction will define a set of numerical values that should be
extracted for each study (e.g. number of subjects, treatment effect, confidence
intervals, etc.). Numerical data are important for any attempt to summarise the results
of a set of primary studies and are a prerequisite for meta-analysis (i.e. statistical
techniques aimed at integrating the results of the primary studies).
Data extraction forms need to be piloted on a sample of primary studies. If several
researchers will use the forms, they should all take part in the pilot. The pilot studies
are intended to assess both technical issues such as the completeness of the forms and
usability issues such as the clarity of user instructions and the ordering of questions.
Electronic forms are useful and can facilitate subsequent analysis.
6.4.2 Contents of Data Collection Forms
In addition to including all the questions needed to answer the review question and
quality evaluation criteria, data collection forms should provide standard information
Name of Reviewer
Date of Data extraction
Title, authors, journal, publication details
Space for additional notes
Kitchenham et al. 364H[21] used the extraction form shown in 365HTable 7 (note the actual form also
included the quality questions).
Table 7 Data Collection form completed for Maxwell et al., 1998
Data item Value Additional notes
Data Extractor
Data Checker
Study Identifier S1
Application domain Space, military and industrial
Name of database European Space Agency (ESA)
Number of projects in
database (including within-
company projects)
Number of cross-company
projects 60
Number of projects in within-
company data set 29
Size metric(s):
FP (Yes/No)
Version used:
LOC (Yes/No)
Version used:
Others (Yes/No)
FP: No
Others: No
Number of companies 37
Number of countries
represented 8 European only
Were quality controls applied
to data collection? No
If quality control, please
How was accuracy
measured? Measures:
P (for model construction only)
r (Correlation between estimate
and actual)
Cross-company model
What technique(s) was used
to construct the cross-
company model?
A preliminary productivity analysis
was used to identify factors for
inclusion in the effort estimation
Generalised linear models (using
SAS). Multiplicative and Additive
models were investigated. The
multiplicative model is a
logarithmic model.
If several techniques were
used which was most
In all cases, accuracy assessment
was based on the logarithmic
models not the additive models.
It can be assumed that
linear models did not work
What transformations if any
were used? Not clear whether the variables
were transformed or the GLM was
used to construct a log-linear
Not important: the log
models were used and they
were presented in the raw
data form – thus any
accuracy metrics were
based on raw data
What variables were
included in the cross-
company model?
KLOC, Language subset, Category
subset, RELY Category is the type of
RELY is reliability as
defined by Boehm (1981)
What cross-validation
method was used? A hold-out sample of 9 projects
from the single company was used
to assess estimate accuracy
Was the cross-company
model compared to a
baseline to check if it was
better than chance?
Yes The baseline was the
correlation between the
estimates and the actuals
for the hold-out.
What was/were the
measure(s) used as
The correlation between the
prediction and the actual for the
single company was tested for
statistical significance. (Note it was
significantly different from zero for
the 20 project data set, but not the
9 project hold-out data set.)
Within-company model
What technique(s) was used
to construct the within-
company model?
A preliminary productivity analysis
was used to identify factors for
inclusion in the effort estimation
Generalised linear models (using
SAS). Multiplicative and Additive
models were investigated. The
multiplicative model is a
logarithmic model.
If several techniques were
used which was most
In all cases, accuracy assessment
was based on the logarithmic
models not the additive models.
It can be assumed that
linear models did not work
What transformations if any
were used? Not clear whether the variables
were transformed or the GLM was
used to construct a log-linear
Not important: the log
models were used and they
were presented in the raw
data form – thus any
accuracy metrics were
based on raw data
What variables were
included in the within-
company model?
KLOC, Language subset, Year
What cross-validation A hold-out sample of 9 projects
method was used from the single company was used
to assess estimate accuracy
What was the accuracy
obtained using the cross-
company model?
Accuracy on main single company
data set (log model):
n=11 (9 projects omitted)
Accuracy on single company hold
out data set
n=4 (5 projects omitted)
R=0.16 (n.s)
Using the 79 cross-
company projects, Maxwell
et al. identified the best
model for that dataset and
the best model for the
single company data. The
two models were identical.
This data indicates that for
all the single company
Pred(25)=26.7% (4 of 15)
What was the accuracy
obtained using the within-
company model?
Accuracy on main single company
data set (log model):
n=14 (6 projects omitted)
Accuracy on single company hold
out data set
n=6 (3 projects omitted)
Pred(25)=50% (3 of 6)
What measure was used to
check the statistical
significance of prediction
accuracy (e.g. absolute
residuals, MREs)?
Estimated and actual effort
What statistical tests were
used to compare the results?
r, correlation between the
prediction and the actual
What were the results of the
Data Summary
Data base summary (all
projects) for size and effort
Effort min: 7.8 MM
Effort max: 4361 MM
Effort mean: 284 MM
Effort median: 93 MM
Size min: 2000 KLOC
Size max: 413000 KLOC
Size mean: 51010 KLOC
Size median: 22300 KLOC
KLOC: non-blank, non-
comment delivered 1000
lines. For reused code
Boehm’s adjustment were
made (Boehm, 1981).
Effort was measured in
man months, with 144 man
hours per man month
With-company data
summary for size and effort
Effort min:
Effort max:
Effort mean:
Effort median:
Size min:
Size max:
Size mean:
Size median:
Not specified
Jørgensen 366H[17] extracted design factors and primary study results. Design factors included:
Study design
Estimation method selection process
Estimation models
Calibration level
Model use expertise and degree of mechanical use of model
Expert judgment process
Expert judgement estimation expertise
Possible motivational biases in estimation situation
Estimation input
Contextual information
Estimation complexity
Fairness limitations
Other design issues
Study results included:
Other results
Jørgensen’s article includes the completed extraction form for each primary study.
6.4.3 Data extraction procedures
Whenever feasible, data extraction should be performed independently by two or
more researchers. Data from the researchers must be compared and disagreements
resolved either by consensus among researchers or arbitration by an additional
independent researcher. Uncertainties about any primary sources for which agreement
cannot be reached should be investigated as part of any sensitivity analyses. A
separate form must be used to mark and correct errors or disagreements.
If several researchers each review different primary studies because time or resource
constraints prevent all primary papers being assessed by at least two researchers, it is
important to employ some method of checking that researchers extract data in a
consistent manner. For example, some papers should be reviewed by all researchers
(e.g. a random sample of primary studies), so that inter-researcher consistency can be
For single researchers such as PhD students, other checking techniques must be used.
For example supervisors could perform data extraction on a random sample of the
primary studies and their results cross-checked with those of the student.
Alternatively, a test-retest process can be used where the researcher performs a
second extraction from a random selection of primary studies to check data extraction
Kitchenham et al. 367H[21] assigned one person to be the data extractor who completed the data
extraction form and another person to be the data checker who confirmed that the data on
extraction form were correct. Because Kitchenham and Mendes co-authored some of the
primary studies, they also ensured that the data extractor was never a co-author of the
primary study. Any disagreements were examined and an agreed final data value recorded.
As a single researcher, Jørgensen 368H[17] extracted all the data himself. However, he sent the
data from each primary study to an author of the study and requested that they inform him if
any of the extracted data was incorrect.
6.4.4 Multiple publications of the same data
It is important not to include multiple publications of the same data in a systematic
review synthesis because duplicate reports would seriously bias any results. It may be
necessary to contact the authors to confirm whether or not reports refer to the same
study. When there are duplicate publications, the most complete should be used. It
may even be necessary to consult all versions of the report to obtain all the necessary
6.4.5 Unpublished data, missing data and data requiring manipulation
If information is available from studies in progress, it should be included providing
appropriate quality information about the study can be obtained and written
permission is available from the researchers.
Reports do not always include all relevant data. They may also be poorly written and
ambiguous. Again the authors should be contacted to obtain the required information.
Sometimes primary studies do not provide all the data but it is possible to recreate the
required data by manipulating the published data. If any such manipulations are
required, data should first be reported in the way they were published. Data obtained
by manipulation should be subject to sensitivity analysis.
6.4.6 Lessons learned about Data Extraction
Brereton et al. 369H[5] identified two issues of importance during data extraction:
Having one reader act as data extractor and one act as data checker may be
helpful when there are a large number of papers to review.
Review team members must make sure they understand the protocol and the
data extraction process.
6.5 Data Synthesis
Data synthesis involves collating and summarising the results of the included primary
studies. Synthesis can be descriptive (non-quantitative). However, it is sometimes
possible to complement a descriptive synthesis with a quantitative summary. Using
statistical techniques to obtain a quantitative synthesis is referred to as meta-analysis.
Description of meta-analysis methods is beyond the scope of this document, although
techniques for displaying quantitative results will be described. (To learn more about
meta-analysis see 370H[7].)
The data synthesis activities should be specified in the review protocol. However,
some issues cannot be resolved until the data is actually analysed, for example, subset
analysis to investigate heterogeneity is not required if the results show no evidence of
6.5.1 Descriptive (Narrative) synthesis
Extracted information about the studies (i.e. intervention, population, context, sample
sizes, outcomes, study quality) should be tabulated in a manner consistent with the
review question. Tables should be structured to highlight similarities and differences
between study outcomes.
It is important to identify whether results from studies are consistent with one another
(i.e. homogeneous) or inconsistent (e.g. heterogeneous). Results may be tabulated to
display the impact of potential sources of heterogeneity, e.g. study type, study quality,
and sample size.
Kitchenham et al. 371H[21] tabulated the data from the primary studies in three separate tables
based on the outcome of the primary study: no significant difference between the cross-
company model and the within company model, within-company model significantly better
than the cross-company model and no statistical tests performed. They also highlighted
studies that they believed should be excluded from the synthesis because they were
complete replications in terms of the cross-company database and the within company
database because they did not offer additional independent evidence.
They concluded that small companies producing specialised (niche) software would not
benefit from using a cross-company estimation model. Large companies producing
applications of similar size range to the cross-company projects might find cross-company
models helpful.
Jørgensen 372H[17] tabulated the studies according to the relative accuracy of the model and the
experts. Thus he considered the accuracy of the most accurate expert and least accurate
expert compared with the most accurate and least accurate models. He also considered the
average accuracy of the models and the experts. He coded the studies chronologically (as did
Kitchenham et al.), so it was possible to look for possible associations with study age and
He concluded that models are not systematically better than experts for software cost
estimation, possibly because experts possess more information than models or it may be
difficult to build accurate software development estimation models. Expert opinion is likely to
be useful if models are not calibrated to the company using them and/or experts have access
to important contextual information that they are able to exploit. Models (or a combination of
models and experts) may be useful when there are situational biases towards overoptimism,
experts do not have access to large amounts of contextual information, and/or models are
calibrated to the environment.
6.5.2 Quantitative Synthesis
Quantitative data should also be presented in tabular form including:
Sample size for each intervention.
Estimates effect size for each intervention with standard errors for each effect.
Difference between the mean values for each intervention, and the confidence
interval for the difference.
Units used for measuring the effect.
However, to synthesise quantitative results from different studies, study outcomes
must be presented in a comparable way. Medical guidelines suggest different effect
measures for different types of outcome.
Binary outcomes (Yes/No, Success/Failure) can be measured in several different
Odds. The ratio of the number of subjects in a group with an event to the number
without an event. Thus if 20 projects in a group of 100 project failed to achieve
budgetary targets, the odds would be 20/80 or 0.25.
Risk (proportion, probability, rate) The proportion of subjects in a group observed
to have an event. Thus, if 20 out of 100 projects failed to achieve budgetary
targets, the risk would be 20/100 or 0.20.
Odds ratio (OR). The ratio of the odds of an event in the experimental (or
intervention) group to the odds of an event on the control group. An OR equal to
one indicates no difference between the control and the intervention group. For
undesirable outcomes a value less than one indicates that the intervention was
successful in reducing risk, for a desirable outcome a value greater than one
indicates that the intervention was successful in reducing risk.
Relative risk (RR) (risk ratio, rate ratio). The ratio of risk in the intervention
group to the risk in the control group. An RR of one indicates no difference
between comparison groups. For undesirable events an RR less than one indicates
the intervention was successful, for desirable events an RR greater than one
indicates the intervention was successful.
Absolute risk reduction (ARR) (risk difference, rate difference). The absolute
difference in the event rate between the comparison groups. A difference of zero
indicates no difference between the groups. For an undesirable outcome an ARR
less than zero indicates a successful intervention, for a desirable outcome an ARR
greater than zero indicates a successful intervention.
Each of these measures has advantages and disadvantages. For example, odds and
odds ratios are criticised for not being well-understood by non-statisticians (other than
gamblers), whereas risk measures are generally easier to understand. Alternatively,
statisticians prefer odds ratios because they have some mathematically desirable
properties. Another issue is that relative measures are generally more consistent than
absolute measures for statistical analysis, but decision makers need absolute values in
order to assess the real benefit of an intervention.
Effect measures for continuous data include:
Mean difference. The difference between the means of each group (control and
intervention group).
Weighted mean difference (WMD). When studies have measured the difference
on the same scale, the weight given to each study is usually the inverse of the
study variance
Standardised mean difference (SMD). A common problem when summarising
outcomes is that outcomes are often measured in different ways, for example,
productivity might be measured in function points per hour, or lines of code per
day. Quality might be measured as the probability of exhibiting one or more faults
or the number of faults observed. When studies use different scales, the mean
difference may be divided by an estimate of the within-groups standard deviation
to produce a standardised value without any units. However, SMDs are only valid
if the difference in the standard deviations reflect differences in the measurement
scale, not real differences among trial populations.
6.5.3 Presentation of Quantitative Results
The most common mechanism for presenting quantitative results is a forest plot, as
shown in 373HFigure 1. A forest plot presents the means and variance of the difference for
each study. The line represents the standard error of the difference, the box represents
the mean difference and its size is proportional to the number of subjects in the study.
A forest plot may also be annotated with the numerical information indicating the
number of subjects in each group, the mean difference and the confidence interval on
the mean. If a formal meta-analysis is undertaken, the bottom entry in a forest plot
will be the summary estimate of the treatment difference and confidence interval for
the summary difference.
374HFigure 1 represents the ideal result of a quantitative summary, as the results of the
studies basically agree. There is clearly a genuine treatment effect and a single overall
summary statistic would be a good estimate of that effect. If effects were very
different from study to study, our results would suggest heterogeneity. A single
overall summary statistics would probably be of little value. The systematic review
should continue with an investigation of the reasons for heterogeneity.
To avoid the problems of post-hoc analysis (i.e. “fishing” for results), researchers
should identify possible sources of heterogeneity when they construct the review
protocol. For example, studies of different types may have different results, so it is
often useful to synthesise the results of different study types separately and assess
whether the results are consistent across the different study types.
Figure 1 Example of a forest plot
Study 1
Study 2
Study 3
-0.2 -0.1 0 0.1 0.2
Favours control Favours intervention
6.5.4 Qualitative Synthesis
Synthesizing qualitative studies involves trying to integrate studies comprising natural
language results and conclusions, where different researchers may have used terms
and concepts with subtly (or grossly) different meanings. Noblit and Hare 375H[23]
propose three approaches to qualitative synthesis:
Reciprocal translation. When studies are about similar things and researchers are
attempting to provide an additive summary, synthesis can be achieved by
“translating” each case into each of the other cases.
Refutational Synthesis. When studies are implicitly or explicitly refutations of
each other, it is necessary to translate both the individual studies and the
refutations allowing the refutations to be analysed in detail.
Line of argument synthesis. This approach is used when researchers are concerned
about what they can infer about a topic as a whole from a set of selective studies
that look at a part of the issue. This analysis is a two part one. First the individual
studies are analysed, then an attempt is made to analyse the set of studies as a
whole. This is rather similar to a descriptive synthesis. Issues of importance are
identified and the approach to each issue taken by each study is documented and
6.5.5 Synthesis of qualitative and quantitative studies
When researchers have a systematic literature review that includes quantitative and
qualitative studies, they should:
Synthesise the quantitative and qualitative studies separately.
Then attempt to integrate the qualitative and quantitative results by investigating
whether the qualitative results can help explain the quantitative results. For
example qualitative studies can suggest reasons why a treatment does or does not
work in specific circumstances.
As yet we have no published software engineering SLRs that have combined a
qualitative survey and a quantitative survey. However, Sutcliffe et al. 376H[28] provide an
example of such a study in their survey of children and healthy eating. They
performed three syntheses:
1. A statistical meta-analysis of studies which attempted to increase children’s
consumption of fruit and vegetables.
2. A thematic qualitative synthesis of studies focused on children’s views of
healthy eating.
3. A “cross-study synthesis” that used the results of the qualitative synthesis to
interpret the findings of the meta-analysis.
6.5.6 Sensitivity analysis
Sensitivity analysis is important whether you have undertaken a descriptive or
quantitative synthesis. However, it is usually easier to perform as part of a meta-
analysis (since quantitative sensitivity analysis techniques are well understood). In
such cases, the results of the analysis should be repeated on various subsets of
primary studies to determine whether the results are robust. The types of subsets
selected would be:
High quality primary studies only.
Primary studies of particular types.
Primary studies for which data extraction presented no difficulties (i.e. excluding
any studies where there was some residual disagreement about the data extracted).
The experimental method used by the primary studies.
When a formal meta-analysis is not undertaken but quantitative results have been
tabulated, forest plots can be annotated to identify high quality primary studies, the
studies can be presented in decreasing order of quality or in decreasing study type
hierarchy order. Primary studies where there are queries about the data extracted can
also be explicitly identified on the forest plot, by for example, using grey colouring
for less reliable studies and black colouring for reliable studies.
When you have undertaken a descriptive synthesis, sensitivity analysis is more
subjective, but you should consider what impact excluding poor quality studies or
studies of a particular type would have on your conclusions.
Jørgensen 377H[17] reported the results of field studies as well as the results of all studies based
on the argument that field studies would have more external validity.
In a study of the Technology Acceptance Model (TAM), Turner et al. 378H[29] investigated the
relationship between the TAM variables Perceived Ease of Use (PEU) and PU (Perceived
Usefulness) and Actual Use measured subjectively and objectively. As part of their sensitivity
analysis they investigated the impact on their results of removing primary studies authored by
the researcher who developed the TAM.
6.5.7 Publication bias
Funnel plots are used to assess whether or not a systematic review is likely to be
vulnerable to publication bias. Funnel plots plot the treatment effect (i.e. mean
difference between intervention group and control) against the inverse of the variance
or the sample size. A systematic review that exhibited the funnel shape shown in
379HFigure 2 would be assumed not to be exhibiting evidence of publication bias. It would
be consistent with studies based on small samples showing more variability in
outcome than studies based on large samples. If, however, the points shown as filled-
in black dots were not present, the plot would be asymmetric and it would suggest the
presence of publication bias. This would suggest the results of the systematic review
must be treated with caution.
Figure 2 An example of a funnel plot
Treatment effect
6.5.8 Lessons Learned about Data Synthesis
Brereton et al. 380H[5] identified three issues of importance during data extraction:
IT and software engineering systematic reviews are likely to be qualitative (i.e.
descriptive) in nature.
Even when collecting quantitative information it may not be possible to perform
meta-analysis of IT and software engineering studies because the reporting
protocols vary so much between studies.
Tabulating the data is a useful means of aggregation but it is necessary to explain
how the aggregated data actually answer the research questions.
7. Reporting the review (Dissemination)
The final phase of a systematic review involves writing up the results of the review
and circulating the results to potentially interested parties.
7.1 Specifying the Dissemination Strategy
It is important to communicate the results of a systematic review effectively. For this
reason most guidelines recommend planning the dissemination strategy during the
commissioning stage (if any) or when preparing the systematic review protocol.
Academics usually assume that dissemination is about reporting results in academic
journals and/or conferences. However, if the results of a systematic review are
intended to influence practitioners, other forms of dissemination are necessary. In
1. Practitioner-oriented journals and magazines
2. Press Releases to the popular and specialist press
3. Short summary leaflets
4. Posters
5. Web pages
6. Direct communication to affected bodies.
7.2 Formatting the Main Systematic Review Report
Usually systematic reviews will be reported in at least two formats:
In a technical report or in a section of a PhD thesis.
In a journal or conference paper.
A journal or conference paper will normally have a size restriction. In order to ensure
that readers are able to properly evaluate the rigour and validity of a systematic
review, journal papers should reference a technical report or thesis that contains all
the details.
The structure and contents of reports suggested in 381H[19] are presented in 382HTable 8. This
structure is appropriate for technical reports and journals. For PhD theses, the entries
marked with an asterisk are not likely to be relevant.
7.3 Evaluating Systematic Review Reports
Journal articles will be peer reviewed as a matter of course. Experts review PhD
theses as part of the examination process. In contrast, technical reports are not usually
subjected to any independent evaluation. However, if systematic reviews are made
available on the Web so that results are made available quickly to researchers and
practitioners, it is worth organising a peer review. If an expert panel were assembled
to review the study protocol, the same panel would be appropriate to undertake peer
review of the systematic review report, otherwise several researchers with expertise in
the topic area and/or systematic review methodology should be approached to review
the report.
The evaluation process can use the quality checklists for systematic literature reviews
discussed in Section 5.1.
7.4 Lessons Learned about Reporting Systematic Literature Reviews
Brereton et al. 383H[5] identified two issues of importance during data extraction:
Review teams need to keep a detailed record of decisions made throughout the
review process.
The software engineering community needs to establish mechanisms for
publishing systematic literature reviews which may result in papers that are longer
than those traditionally accepted by many software engineering outlets or that
have appendices stored in electronic repositories.
Staples and Niazi 384H[27] also emphasize the need to keep a record of what happens
during the conduct of the review. They point out that you need to report deviations
from the protocol.
With respect to publishing systematic literature reviews, the Journal of Information
and Software Technology (
has expressed a willingness to publish systematic literature reviews.
Table 8 Structure and Contents of Reports of Systematic Reviews
Section Subsection Scope Comments
Title* The title should be short but informative. It should be based on the
question being asked. In journal papers, it should indicate that the study is
a systematic review.
Authorship* When research is done collaboratively, criteria for determining both who
should be credited as an author, and the order of author’s names should be
defined in advance. The contribution of workers not credited as authors
should be noted in the Acknowledgements section.
The importance of the research
questions addressed by the review.
The questions addressed by the
systematic review.
Methods Data Sources, Study selection, Quality
Assessment and Data extraction.
Results Main finding including any meta-
analysis results and sensitivity
Executive summary
or Structured
Conclusions Implications for practice and future
A structured summary or abstract allows readers to assess quickly the
relevance, quality and generality of a systematic review.
Background Justification of the need for the
Summary of previous reviews.
Description of the software engineering technique being investigated and
its potential importance.
Review questions Each review question should be
specified. Identify primary and secondary review questions. Note this section may be
included in the background section.
Data sources and search
Study selection
Study quality assessment
Data extraction
Review Methods
Data synthesis
This should be based on the research protocol. Any changes to the original
protocol should be reported.
Included and
excluded studies Inclusion and exclusion criteria.
List of excluded studies with rationale
for exclusion.
Study inclusion and exclusion criteria can sometimes best be represented
as a flow diagram because studies will be excluded at different stages in
the review for different reasons.
Findings Description of primary studies.
Results of any quantitative summaries
Details of any meta-analysis.
Sensitivity analysis
Non-quantitative summaries should be provided to summarise each of the
studies and presented in tabular form.
Quantitative summary results should be presented in tables and graphs.
Discussion Principal findings These must correspond to the findings discussed in the results section.
Strengths and Weaknesses Strengths and weaknesses of the
evidence included in the review.
Relation to other reviews, particularly
considering any differences in quality
and results.
A discussion of the validity of the evidence considering bias in the
systematic review allows a reader to assess the reliance that may be placed
on the collected evidence.
Meaning of findings Direction and magnitude of effect
observed in summarised studies.
Applicability (generalisability) of the
Make clear to what extent the results imply causality by discussing the
level of evidence.
Discuss all benefits, adverse effects and risks.
Discuss variations in effects and their reasons (for example are the
treatment effects larger on larger projects).
Practical implications for software
development. What are the implications of the results for practitioners? Conclusions Recommendations
Unanswered questions and
implications for future research.
Acknowledgements* All persons who contributed to the
research but did not fulfil authorship
Conflict of Interest Any secondary interest on the part of the researchers (e.g. a financial
interest in the technology being evaluated) should be declared.
References and
Appendices Appendices can be used to list studies included and excluded from the
study, to document search strategy details, and to list raw data from the
included studies.
8 Systematic Mapping Studies
Systematic Mapping Studies (also known as Scoping Studies) are designed to provide
a wide overview of a research area, to establish if research evidence exists on a topic
and provide an indication of the quantity of the evidence. The results of a mapping
study can identify areas suitable for conducting Systematic Literature Reviews and
also areas where a primary study is more appropriate. Mapping Studies may be
requested by an external body before they commission a systematic review to allow
more cost effective targeting of their resources. They are also useful to PhD students
who are required to prepare an overview of the topic area in which they will be
working. As an example of a mapping study see Bailey et al.’s mapping study which
aimed at investigating the extent to which software design methods are supported by
empirical evidence 385H[3].
The main differences between a mapping study and systematic review are:
Mapping studies generally have broader research questions driving them and often
ask multiple research questions.
The search terms for mapping studies will be less highly focussed than for
systematic reviews and are likely to return a very large number of studies, for a
mapping study however this is less of a problem than with large numbers of
results during the search phase of the systematic review as the aim here is for
broad coverage rather than narrow focus.
The data extraction process for mapping studies is also much broader than the data
extraction process for systematic reviews and can more accurately be termed a
classification or categorisation stage. The purpose of this stage is to classify
papers with sufficient detail to answer the broad research questions and identify
papers for later reviews without being a time consuming task.
The analysis stage of a mapping study is about summarising the data to answer the
research questions posed. It is unlikely to include in depth analysis techniques
such as meta-analysis and narrative synthesis, but totals and summaries. Graphical
representations of study distributions by classification type may be an effective
reporting mechanism.
Dissemination of the results of a mapping study may be more limited than for a
systematic review; limited to commissioning bodies and academic publications,
with the aim of influencing the future direction of primary research.
9 Final remarks
This report has presented a set of guidelines for planning, conducting, and reporting a
systematic review. The previous versions of these guidelines were based on guidelines
used in medical research. However, it is important to recognise that software
engineering research is not the same as medical research. We do not undertake
randomised clinical trials, nor can we use blinding as a means to reduce distortions
due to experimenter and subject expectations. For this reason, this version of the
guidelines has incorporated information from text books authored by researchers from
the social sciences.
These guidelines are intended to assist PhD students as well as larger research groups.
However, many of the steps in a systematic review assume that it will be undertaken
by a large group of researchers. In the case of a single researcher (such as a PhD
student), we suggest the most important steps to undertake are:
Developing a protocol.
Defining the research question.
Specifying what will be done to address the problem of a single researcher
applying inclusion/exclusion criteria and undertaking all the data extraction.
Defining the search strategy.
Defining the data to be extracted from each primary study including quality data.
Maintaining lists of included and excluded studies.
Using the data synthesis guidelines.
Using the reporting guidelines
In our experience this “light” version of a systematic review is manageable for PhD
students. Furthermore, research students often find the well-defined nature of a
systematic review helpful both for initial scoping exercises and for more detailed
studies that are necessary to position their specific research questions.
10 References
[1] Australian National Health and Medical Research Council. How to review the
evidence: systematic identification and review of the scientific literature, 2000.
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[2] Australian National Health and Medical Research Council. How to use the
evidence: assessment and application of scientific evidence. February 2000,
ISBN 0 642 43295 2.
[3] Bailey, J., Budgen, D., Turner, M., Kitchenham, B., Brereton, P. and Linkman,
S. Evidence relating to Object-Oriented software design: A survey. ESEM07.
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results of meta-analysis? Online J. Curr. Clin. Trials, 1997: Doc No 205.
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Khalil, Mohamed. Lessons from applying the systematic literature review
process within the software engineering domain. JSS 80, 2007, pp 571-583.
[6] TBudgenT, David, TStuart ChartersT, TMark TurnerT, TPearl BreretonT, TBarbara
KitchenhamT and TStephen Linkman HT83HInvestigating the Applicability of the
Evidence-Based Paradigm to Software EngineeringHT, TTProceedings of WISER
WorkshopT, ICSE 2006, 7-13, May 2006, ACM Press.
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[13] Hart, Chris. Doing a Literature Review. Releasing the Social Science Research
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Reviews to Support Evidence-based Medicine, The Royal Society of Medicine
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Cross- vs. Within-Company Cost Estimation Studies, IEEE Trans on SE, 33 (5),
pp 316-329.
[22] Lawlor, Debbie A., George Davey Smith, K Richard Bruckdorfer, Devi Kundu,
Shah. Ebrahim. Those confounded vitamins: what can we learn from the
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[23] Noblit, G.W. and Hare, R.D. Meta-Ethnography: Synthesizing Qualitative
Studies. Sage Publications, 1988.
[24] Pai, Madhukar., McCulloch, Michael., Gorman, Jennifer D., Pai, Nitika,
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[25] Petticrew, Mark and Helen Roberts. Systematic Reviews in the Social Sciences:
A Practical Guide, Blackwell Publishing, 2005, ISBN 1405121106
[26] Shadish, W.R., Cook, Thomas, D. and Campbell, Donald, T. Experimental and
Quasi-experimental Designs for Generalized Causal Inference. Houghton
Mifflin Company, 2002.
[27] Staples, M. and Niazi, M. Experiences using systematic review guidelines.
Article available online, JSS.
[28] Sutcliffe, T.J., Harden, K., Oakley, A., Oliver, A., Rees,S., Brunton, R. and
Kavanagh, G. Children and Healthy Eating: A systematic review of barriers and
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[29] Turner, M., Kitchenham, B., Budgen, D., Charters, S. and Brereton, P. A
Systematic Literature Review of the technology Acceptance Model and its
Predictive Capabilities, Keele University and University of Durham Joint
Technical Report, 2007.
Appendix 1 Steps in a systematic review
Guidelines for systematic review in the medical domain have different view of the
process steps needed in a systematic review. The Systematic Reviews Group (UC
Berkeley) present a very detailed process model 386H[24], other sources present a coarser
process. These process steps are summarised in 387HTable 9, which also attempts to
collate the different processes.
Table 9 Systematic review process proposed in different sources
Systematic Reviews Group 388H[24] Australian National
Health and Medical
Research Council
Cochrane Reviewers
Handbook 390H[7] CRD Guidance 391H[19] Petticrew and Roberts 392H[25] Fink 393H[11]
Identification of the need for a
Preparation of a proposal for a
systematic review
Developing a protocol
Development of a review
protocol Define the question & develop
draft protocol
Identify a few relevant studies and
do a pilot study; specify
inclusion/exclusion criteria, test
forms and refine protocol.
Formulation Formulating the problem Refine questions and define
boundaries Select Research
Identify appropriate
databases/sources. Finding Studies Locating and selecting studies
for reviews Identification of research
Define Inclusion/Exclusion
criteria Select Bibliographic
Databases and Web
Choose Search Terms
Run searches on all relevant data
bases and sources.
Save all citations (titles/abstracts)
in a reference manager. Document
search strategy
Selection of studies Find the primary studies Find the studies
Researchers (at least 2) screen
titles & abstracts.
Researchers meet & resolve
Get full texts of all articles.
Researchers do second screen.
Articles remaining after second
screen is the final set for inclusion
Apply Practical
Screening criteria
Assessment of study quality Study quality assessment Assess study quality Apply methodological
Quality Screen
Researchers extract data including
quality data.
Researchers meet to resolve
disagreements on data
Compute inter-rater reliability.
Enter data into database
management software
Appraisal and
selection of studies
Collecting data Data extraction & monitoring
progress Train Reviewers
Pilot the Reviewing
Do the Review
Import data and analyse using
meta-analysis software.
Pool data if appropriate.
Look for heterogeneity.
Summary and
synthesis of
relevant studies
Analysing & presenting results Data synthesis Synthesize the evidence.
Explore heterogeneity and
publication bias
Synthesize the results
Produce a descriptive
review or perform
Interpret & present data.
Discuss generalizability of
conclusions and limitations of the
Make recommendations for
practice or policy, & research.
Determining the
applicability of
Reviewing and
appraising the
Interpreting the results The report and
Getting evidence into practice.
Disseminate the results
Appendix 2 Software Engineering Systematic Literature Reviews
Software engineering SLRs published between 2004 and June 2007 that scored 2 or more on University of York, CRD DARE scale as assessed
by staff working on the Keele University and Durham University EBSE project.
Author Date Title Reference Details Topic type Topic area Quality
Barcelos, R.F., and
Travassos, G.H. 2006 Evaluation approaches for
Software Architectural
Documents: A systematic
Ibero-American Workshop on
Requirements Engineering and
Software Environments (IDEAS).
La Plata, Argentina.
evaluation Software
Dyba, T; Kampenes,
V.B. and Sjoberg,
2006 A systematic review of statistical
power in software engineering
Information and Software
Technology, 48(8), pp 745-755. Research trends Power in SE
Experiments 2.5
Glass, R.L., Ramesh,
V., and Vessey, I 2004 An Analysis of Research in
Computing Disciplines CACM, Vol. 47, No. 6, pp89-94. Research Trends Comparative
trends in CS,
IS and SE
Grimstad, S.,
Jorgensen, M. and
2006 Software effort estimation
terminology: The tower of Babel Information and Software
Technology, 48 (4), pp 302-310 Technology Cost
Estimation 3
Hannay, J E.,
Sjøberg, D.I.K and
Dybå. T
2007 A Systematic Review of Theory
Use in Software Engineering
IEEE Trans on SE, 33 (2), pp 87-
107. Research trends Theory in
Jørgensen, M 2004 A review of studies on expert
estimation of software
development effort,
Journal of Systems and Software,
70 (1-2), pp37-60. Technology Cost
Estimation 3
Jørgensen, M., and
Shepperd, M. 2007 A Systematic Review of
Software Development Cost
Estimation Studies
IEEE Transactions on SE, 33(1),
Research trends Cost
Estimation 3
Kampenes, V.B.,
Dybå, T., Hannay,
J.E. and Sjøberg,
D.I.K. (
2007 A systematic review of effect
size in software engineering
Information and Software
Technology, In press. Research trends Effect size
in SE
Mair, C. and
Shepperd, M. 2005 The consistency of empirical
comparisons of regression and
analogy-based software project
cost prediction
International Symposium on
Empirical Software Engineering
evaluation Cost
Estimation 2
Mendes, E. 2005 A systematic review of Web
engineering research. International Symposium on
Empirical Software Engineering Research Trends Web
Research 2
K.J., Jørgensen, M.
Tanilkan, S.S.,
Gallis,H., Lien, A.C.
and Hove, S.E.
2004 Survey on Software Estimation
in the Norwegian Industry Proceedings Software Metrics
Symposium. Technology
evaluation Cost
Estimation 2
Petersson, H.,
Thelin, T, Runeson,
P, and Wohlin, C.
2004 Capture-recapture in software
inspections after 10 years
research – theory, evaluation and
Journal of Systems and Software,
72, 2004, pp 249-264 Technology
evaluation Capture-
recapture in
Runeson, P.,
Andersson, C.,
Thelin, T., Andrews,
A. and Berling, T.
2006 What do we know about Defect
Detection Methods? IEEE Software, 23(3) 2006, pp 82-
86. Technology
evaluation Testing
methods 2
Sjoeberg, D.I.K.,
Hannay, J.E.,
Hansen, O.,
Kampenes, V.B.,
Karahasanovic, A.,
Liborg, N.K. and
Rekdal, A.C.
2005 A survey of controlled
experiments in software
IEEE Transactions on SE, 31 (9),
2005, pp733-753. Research trends SE
experiments 2
Zannier, C, Melnick,
G. and Maurer, F. 2006 On the Success of Empirical
Studies in the International
Conference on Software
ICSE06, pp 341-350
Research Trends Empirical
studies in
Appendix 3 Protocol for a Tertiary study of Systematic
Literature Reviews and Evidence-based Guidelines in IT and
Software Engineering
Barbara Kitchenham, Pearl Brereton, David Budgen, Mark Turner, John Bailey and
Stephen Linkman
At ICSE04, Kitchenham et al. (2004) Suggested software engineering researchers
should adopt “Evidence-based Software Engineering” (EBSE). EBSE aims to apply
an evidence-based approach to software engineering research and practice. The ICSE
paper was followed-up by a paper at Metrics05 (Jørgensen et al., 2005) and an article
in IEEE Software (Dybå et al., 2005).
Following these papers, staff at the Keele University School of Computing and
Mathematics proposed a research project to investigate the feasibility of EBSE. This
proposal was funded by the UK Economics and Physical Science Research Council
(EPSRC). The proposal was amended to include the Department of Computer
Science, University of Durham when Professor David Budgen moved to Durham. The
EPSRC have now funded a joint Keele and Durham follow-on project (EPIC).
The purpose of the study described in this protocol is to review the current status of
EBSE since 2004 using a tertiary study to review articles related to EBSE in particular
articles describing Systematic Literature reviews (SLRs)
Evidence-based research and practice was developed initially in medicine because
research indicated that expert opinion based medical advice was not as reliable as
advice based on scientific evidence. It is now being adopted in many domains e.g.
Criminology, Social policy, Economics, Nursing etc. Based on Evidence-based
medicine, the goal of Evidence-based Software Engineering is:
“To provide the means by which current best evidence from research can be
integrated with practical experience and human values in the decision making
process regarding the development and maintenance of software.” (Dybå et al.,
In this context evidence is defined as a synthesis of best quality scientific studies on a
specific topic or research question. The main method of synthesis is a Systematic
Literature Review (SLR). In contrast to an ad hoc literature review, an SLR is a
methodologically rigorous review of research results.
Research Questions
The research questions to be addressed by this study are:
How much EBSE activity has there been since 2004?
What research topics are being addressed?
Who is leading EBSE research?
What are the limitations of current research?
Search Process
The search process is a manual search of specific conference proceedings and journal
papers since 2004. The nominated journals and conferences are shown in the
following Table.
Sources to be Searched
Source Responsible
Information and Software Technology
Journal of Systems and Software Kitchenham
IEEE Transactions on Software
Engineering Kitchenham
IEEE Software Kitchenham
Communications of the ACM (CACM) Brereton
ACM Surveys Brereton
Transactions on Software Engineering
Methods (TOSEM) Brereton
Software Practice and Experience Budgen & Kitchenham
Empirical Software Engineering Journal
(ESEM) Budgen
IEE Proceedings Software (now IET
Software) Kitchenham
Proceedings International Conference on
Software Engineering (ICSE 04, 05, 06,
Linkman & Kitchenham &
Proceedings International Seminar of
Software Metrics (Metrics04, Metrics05) Kitchenham & Brereton
Proceedings International Seminar on
Empirical Software Engineering (ISESE
04, 05, 06)
Kitchenham & Brereton
Specific researchers will also be contacted directly:
Dr Magne Jørgensen
Professor Guilherme Travassos.
Inclusion criteria
Articles on the following topics, published between Jan 1P
P 2004 and June 30th 2007,
will be included
Systematic Literature Reviews (SLRs) i.e. Literature surveys with defined
research questions, search process, data extraction and data presentation
Meta-analyses (MA)
Exclusion Criteria
The following types of papers will be excluded
Informal literature surveys (no defined research questions, no search process, no
defined data extraction or data analysis process).
Papers discussing process of EBSE.
Papers not subject to peer-review.
When an SLR has been published in more than one journal/conference, the most
complete version of the survey will be used.
Primary study selection process
The results will be tabulated as follows:
Number of papers per year per source
Number of candidate papers per year per source
Number of selected papers per year per source.
The relevant candidate and selected studies will be selected by a single researcher.
The rejected studies will be checked by another researcher. We will maintain a list
candidate papers that were rejected with reasons for the rejection.
Quality Assessment
Each SLR will be evaluated using the York University, Centre for Reviews and
Dissemination (CDR) Database of Abstracts of Reviews of Effects (DARE) criteria
( The criteria are based on
four questions:
Are the review’s inclusion and exclusion criteria described and appropriate?
Is the literature search likely to have covered all relevant studies?
Did the reviewers assess the quality/validity of the included studies?
Were the basic data/studies adequately described?
The questions are scored as follows:
Question 1: Y (yes), the inclusion criteria are explicitly defined in the paper, P
(Partly), the inclusion criteria are implicit; N (no), the inclusion criteria are not
defined and cannot be readily inferred.
Question 2: Y, the authors have either searched 4 or more digital libraries and
included additional search strategies or identified and referenced all journals
addressing the topic of interest; P, the authors have searched 3 or 4 digital
libraries with no extra search strategies, or searched a defined but restricted set
of journals and conference proceedings; N, the authors have search up to 2
digital libraries or an extremely restricted set of journals.
Question 3: Y, the authors have explicitly defined quality criteria and extracted
them from each primary study; P, the research question involves quality issues
that are addressed by the study; N no explicit quality assessment of individual
papers has been attempted.
Question 4: Y Information is presented about each paper; P only summary
information is presented about individual papers; N the results of the individual
studies are not specified.
The scoring procedure is Y=1, P=0.5 and N or Unknown=0.
The data will be extracted by one researcher and checked by another.
Data Collection
The data extracted from each paper will be:
The source (i.e. the conference or journal).
The year when the paper was published. Note if the paper was published in
several difference sources both dates will be recorded and the first date will be
used in any analysis. This is necessary in order to track the EBSE activity over
Classification of paper
o Type (Systematic Literature Review SLR, Meta-Analysis MA).
o Scope (Research trends or specific research question).
Main software engineering topic area.
The author(s) and affiliation (organisation and country).
Research question/issue.
Whether the study referenced an EBSE paper or the SLR Guidelines
(Kitchenham, 2004).
Whether the study resulted in evidence-based practitioner guidelines.
The number of primary studies used in the SLR/MA
Summary of paper.
Quality score for the study.
The data will be extracted by one researcher and checked by another.
Data Analysis
The data will be tabulated (ordered alphabetically by the first author name) to show
the basic information about each study. The number of studies in each major category
will be counted.
The tables will be reviewed to answer the research questions and identify any
interesting trends or limitations in current EBSE-related research as follows:
Question 1 How much EBSE activity has there been since 2004? This will be
addressed by simple counts of the number of EBSE related papers per year.
Question 2 What research topics are being addressed? This will be addressed by
counting the number of papers in each topic area. We will identify whether any
specific topic areas that have a relatively large number of SLRs.
Question 3 Who is leading EBSE research? We will investigate whether any
specific organisation of researches have undertaken a relatively large number of
Question 4 What are the limitations of current research? We will review the
range of SE topics, the scope of SLRs and the quality of SLRs to determine
whether there are any observable limitations. We will also investigate whether
the quality of studies is increasing over time by plotting the quality score against
the first publication date, and whether the quality of studies has been influenced
by the SLR guidelines (by comparing the average quality score of SLRs that
referenced the guidelines with the average score of SLRs that did not reference
the guidelines).
The results of the study should be of interest to the software engineering community
as well as researchers interested in EBSE. For that reason we plan to report the results
on a Web page. We will also document the full result of the study in a joint Keele
University and University of Durham technical report. A short version of the study
will be submitted to IEEE Software.
1. Barbara Kitchenham, Tore Dybå and Magne Jørgensen. (2004) Evidence-based
Software Engineering. Proceedings of the 26th International Conference on
Software Engineering, (ICSE ’04), IEEE Computer Society, Washington DC,
USA, pp 273 – 281 (ISBN 0-7695-2163-0).
2. Kitchenham, B. Procedures for Performing Systematic Reviews. Joint Technical
Report, Keele University TR/SE-0401 and NICTA 0400011T.1, July 2004.
3. Tore Dybå, Barbara Kitchenham, and Magne Jørgensen. Evidence-based
Software Engineering for Practitioners, IEEE Software, Volume 22 (1) January,
2005, pp58-65.
4. Magne Jørgensen, Tore Dybå, and Barbara Kitchenham. Teaching Evidence-
Based Software Engineering to University Students, 11th IEEE International
Software Metrics Symposium (METRICS'05), 2005, p. 24.
... A Revisão Sistemática de Literatura (RSL) compartilha com a Análise de Conteúdo (AC), entre outras características, as modulações normativas da replicabilidade e da confiabilidade (Kitchenham, 2007;Wilson, 2009). Não por acaso, essas metodologias têm sido adotadas em conjunto por diversas pesquisas Simões;Voegeli, 2013;Araújo;Seabra, 2021). ...
... Reconhecendo-se a importância desses princípios para caracterizar uma RSL, este trabalho tem um objetivo bifurcado e entrelaçado, pois estabelece uma proposta baseada no que foi feito anteriormente, ao mesmo tempo que visa aperfeiçoar o rigor metodológico das RSLs mediante a incorporação e adaptação de procedimentos da análise de conteúdo categorial (Kitchenham, 2007;Sampaio, 2021;Wilson, 2009) . No caso, o entrelaçamento ocorre a partir do momento que essa incorporação é realizada na própria RSL que realizamos para descobrir como a área da Comunicação e Informação tem realizado suas RSLs e, com isso, nossa proposta se assenta na distância relativa entre o que propomos e realizamos e aquilo que encontramos. ...
... Sendo assim, as RSLs devem, para fins de transparência e replicabilidade, apresentar, entre outros, informações sobre quais as bases de indexação utilizadas, os termos de busca, os critérios de inclusão e/ou exclusão, assim como as categorias utilizadas para sintetizar o conjunto dos trabalhos selecionados (Kitchenham, 2007;Wilson, 2009;Souza;Aganette, 2020). ...
Full-text available
Resumo Este trabalho apresenta uma análise da qualidade metodológica das revisões sistemáticas de literatura lusófonas, na área da Comunicação e Informação, utilizando princípios e técnicas desta metodologia comuns à análise de conteúdo, notadamente as relativas à replicabilidade e à confiabilidade. Para isso, 4 bases de indexação foram utilizadas: Web of Science, Scopus, DOAJ e SciELO, sob delimitação temporal específica (2010-2021). Mediante o desenvolvimento de um livro de códigos e realização de testes de confiabilidade, 49 trabalhos foram selecionados e analisados em relação a informações sobre (a) bases utilizadas; (b) termos de busca; (c) delimitação temporal (quantidade de anos); (d) número de trabalhos analisados; (e) critérios de inclusão e/ou exclusão; (f) replicabilidade; (g) confiabilidade. Os principais resultados indicam uma reduzida preocupação dos trabalhos analisados em incorporar ou reportar procedimentos responsivos à confiabilidade (n=3). Por outro lado, a maior parte dos estudos (n=40) mostrou-se, em linhas gerais, replicável. A principal inovação e contribuição desta pesquisa consistiu em propor e testar um procedimento de incremento da confiabilidade das revisões sistemáticas mediante consulta aos autores dos trabalhos revisados. Destaca-se, para além do caráter inovador de tal procedimento, os ganhos epistêmicos por ele trazidos. Discute-se, ao final, as implicações dos resultados para pesquisas futuras, indicando os possíveis ganhos que as estratégias metodológicas concebidas e testadas neste trabalho oferecem para balizar o rigor metodológico das revisões sistemáticas de literatura.
... Given the growth of works interested in this theme and to find the main findings and research gaps related to it, a systematic review was conducted using a systematic mapping approach based on the PRISMA methodology [8]. This allowed for a description, critical review, and synthesis of findings in a reproducible manner for future research [9]. ...
... Inclusion and exclusion criteria were established to identify relevant studies (Table 4) and based on these criteria, the most relevant articles on the topic were selected. From here, the process followed three phases: identification, screening, and inclusion [8]. CE6: It is not published in a high-impact journal CI7: It is possible to access the full publication. ...
... Inclusion phase: In the third phase, a second screening was carried out using a quality checklist (Table 5). It was based on a checklist [8] of 6 questions with predefined answers and a score associated with each answer (Yes = 1 / Partially = 0.5 / No = 0). A cut-off score of 3 points was established, and studies evaluated with a score below this were discarded from the final review. ...
Full-text available
Nowadays, the European Union and the governments of the different countries have focused on the development of the Sustainable Development Goals (SDG) and the 2030 agenda, something that has been translated into education itself. Video Games, Gamification, and Game Based Learning have become different strategies and tools to enhance the learning process and some of the growing approaches used by teachers to develop sustainable education in the classrooms. This research aims to analyze the characteristics to promote sustainability in education using games and technology, specifically its learning benefits for Higher Education. A systematic review of the literature was conducted following the PRISMA methodology. At first, 2025 documents were found which, after the filtering phases, the number of articles has been reduced to nine, which subsequently were analyzed in depth. The results indicated that among the benefits of the use of games mediated by technologies are the following: it favors education for sustainability and it promotes the educational inclusion and the work of various social skills such as collaborative and cooperative work. Also, showed an increase of the number of publications between 2019 and 2023, reflecting the growing interest in the topic. However, there are some research gap in this field.
... In crafting this chapter, a systematic review of literature in the mixed reality spectrum was considered as it provided the opportunity to objectively analyse findings from several studies. A three-step process was followed for the review as suggested in Kitchenham [30], including the planning phase, the review phase, and the reporting phase. ...
... The list of identified journals was validated by the authors to ensure that articles covered the relevant integration of technology and were peer-reviewed. The guidelines suggested by Kitchenham [30] were followed in selecting studies based on the key elements of K-12 education and MR/XR/AR. All authors were involved in extracting and discussing the articles to exclude all disagreements. ...
The recent development of applications for virtual reality (VR), augmented reality (AR) and mixed reality (MR) in education has stimulated interest in how these technologies can support teaching and learning at the school level. Of particular interest, is what pedagogies could be applied such that the affordances of these technologies are optimally exploited in supporting content understanding, long-term memory retention, improved task performance, and increased motivation and engagement for learners. This chapter is a systematic review of 18 peer-reviewed journal articles from the last decade starting 2011–2021 September related to MR pedagogy in K-12 education. The main aim of the review is to establish pedagogical approaches, theories, and the impact of integrating MR in K-12 education. Data from selected articles analysed by means of content analysis revealed that though most studies adopt interactive learner-centered pedagogies like inquiry-based, activity-based, discovery, and collaborative learning for MR interventions, they are quite inexplicit about the pedagogical dimensions of the MR-enhanced learning interventions. Based on the findings of this review, we propose a guiding framework for pedagogical processes to be followed in integrating MR technologies for K-12 teaching and make recommendations on future research that can be pursued in this area.
... Given the growth of works interested in this theme, and to find the main findings and research gaps related to it, a systematic review was conducted using a systematic mapping approach based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, which transparently documents why the review was conducted, the process of searching and compiling the sample papers, and what findings were found [14]. This allows for a description, critical review, and synthesis of the findings in a reproducible manner for future research [15,16]. For this review, four mapping questions were proposed to help contextualize the research topic (Box 1), along with eight research questions to critically analyze the phenomenon under study (Box 2). ...
... A cutoff score of 3 points was established, and studies evaluated with a score below this were discarded from the final review. Table 2. Quality criteria used in the second screening [16]. 2. Has the study been designed to achieve these objectives? ...
Full-text available
Today, the European Union and the governments of its constituent countries are focused on the development of the Sustainable Development Goals (SDGs) and the 2030 agenda—something that has been translated into education itself. Video games, gamification, and game-based learning have become different strategies and tools to enhance the learning process and some of the growing approaches used by teachers to develop sustainable education in classrooms. This research aims to analyze the characteristics to promote sustainability in education using games and technology, specifically in terms of learning benefits for higher education. A systematic review of the literature was conducted following the PRISMA methodology. At first, 2025 documents were found; after the filtering phases, the number of articles was reduced to 9, which were subsequently analyzed in depth. The results indicated that the benefits of the use of games mediated by technologies include the following: it favors education for sustainability, and it promotes the educational inclusion and the work of various social skills, such as collaborative and cooperative work. Also, there was an increase in the number of publications between 2019 and 2023, reflecting the growing interest in the topic. However, there are some research gaps in this field.
... For the proposed objective to be achieved, the SLR was guided by a research protocol, complying with the methodological rigor that an SLR requires in order to map and analyze relevant studies based on the protocols proposed by Tranfield, Denyer, and Smart (2003) and Kitchenham (2007). Hence, the research questions that guided this study were as follows: (i) What are the potential impacts of Industry 4.0 manufacturing? ...
... The SLR was based on the protocols proposed by Tranfield et al. (2003) and Kitchenham (2007). The planning stage begins by identifying the research need and specifying the question(s) and objective(s) discussed in the introduction. ...
Full-text available
The concept of Industry 4.0 emerged in Germany as a strategy for innovation and recovery of economic performance and has spread worldwide. The digital transformation proposed by Industry 4.0 is driven by intelligent manufacturing processes, digitalization, flexibility, integration of systems, and real-time analysis of big data generating intelligent processes and services oriented to customer needs. However, despite the opportunities produced by technological innovation, the fourth industrial revolution has established an environment of uncertainty in the labor market and business models reflected throughout the social sphere. Thus, this study aimed to identify the potential impacts and challenges of Industry 4.0 through a systematic literature review (SLR). Our findings pointed to seven potential impacts of Industry 4.0 on manufacturing: (i) environmental, (ii) competitive, (iii) economic, (iv) education, (v) labor market, (vi) business models, and (vii) social. Additionally, six potential challenges faced by manufacturing in adopting digital transformation were found: (i) management, (ii) government, (iii) implementation, (iv) workforce, (v) operation, and (vi) security. The results indicated that accurate understanding of the concepts and scope of Industry 4.0 are under construction and that knowledge sharing will be decisive in shaping the future of the transition from current business models to smart manufacturing.
... To conduct the collection, analysis, and categorization of the existing information and knowledge on a specific field, what are known as methodologies for literature analysis or review are usually followed. In the specific case of this study, we have decided to adopt the aspects of the Systematic Mapping Study (SMS) methodology, as established by the guidelines defined by [38] and considering examples of application such as [39]. Likewise, we have also followed the lessons learned for data extraction and analysis identified by [40]. ...
... The scientific method of systematic research is considered to be superior when compared to conventional literature reviews as it enhances certain aspects such as consistency, replicability, reliability, and validity [73] and reduces redundancies in the published literature, allowing disparities and trends for future studies to be identified. The review process followed here is conducted within the 'Preferred Reporting Items for Systematic Reviews and Meta-Analyses' (PRISMA) framework and can be divided into three concrete steps [42]: ...
Full-text available
Since its debut in the field of education nearly three decades ago, Artificial Intelligence (AI) has been considered as a powerful tool to facilitate new paradigms for instructional design and innovative educational practice in the form of intelligent tutoring systems, chatbots, teaching robots and adaptive learning systems among others. Recent technological advances in the adjacent areas of natural language processing, machine learning and computer graphics focusing primarily on design features that can improve their human-like qualities of naturalness and believability as human interlocutors have also amplified new application opportunities for Intelligent Virtual Agents (IVAs) or Animated Pedagogical Agents (APAs) within the area of Intelligent Computer-Assisted Language Learning (ICALL). Although AI-powered IVAs hold the potential to enhance the learning process in nearly any knowledge domain and personalize automation in teaching by embodying different roles in the learning environment, strikingly few studies have empirically attempted to assess IVAs impact on L2 learners’ academic achievement when learning English as a Foreign (EFL) so far. This study addresses this issue via a systematic review of relevant interventionist IVA studies that were conducted in EFL settings and published within the 2015-2020 timeframe examining IVAs key affordances, major barriers in their adoption for language learning purposes, and the CALL research trends currently prevalent on the topic. Pedagogical implications for the effective implementation of IVA technology in L2 contexts are discussed and future research avenues in the area are highlighted.
Crowdsource software development has become more and more popular in recent years in the software industry. Crowdsourcing is an open‐call technique for outsourcing tasks to a broad and undefined crowd. Crowdsourcing provides numerous advantages including reduced costs, fast project completion, talent identification, diversity of solutions, top‐quality, and access to problem‐solving creativity. Despite of the benefits gained from crowdsourcing, there are numerous issues like lack of experienced workers, lack of confidentiality, copyright issues, software sustainability, and so forth. There is also less focus on the long‐term sustainability of software development because of new ideas emerging in crowdsourcing software development. Furthermore, in literature, lack of guidelines towards sustainable software crowdsourcing is highlighted as one of the limitations in the software standards. This study aims to identify the factors that influence sustainability aspects in crowdsourced software development. We have conducted a systematic literature review for identification of these factors. In this paper, we present findings of the systematic literature review in the form of a list of 11 factors extracted from a sample of 45 finally selected papers. Among these factors, six of the factors are ranked as critical factors. These critical factors are “Lack of coding standard in documentation,” “Use of popular programming tools,” “Crowd Lack of knowledge and awareness about sustainability,” “Energy‐efficient coding,” “Lack of awareness about sustainable software engineering practices,” and “Lack of coordination/communication between client and crowd.”
The transition of education from face-to-face to electronic learning (e-learning) has been accompanied by the application of artificial intelligence and machine learning techniques to improve teaching, learning and assessment processes. Learning Management Systems (LMS) are used to conduct electronic learning (e-learning), and to facilitate student assessment through automatic generation of examinations from a question bank. However, the perceived low quality of these examinations has led them to be used for formative assessments and not for summative assessments. One way to ensure that high quality exams are generated by LMS systems would be to ensure that the questions cover different levels of difficulty as specified by an educational taxonomy. One commonly used taxonomy is Bloom’s Taxonomy, later updated to the Revised Bloom’s Taxonomy (RBT). In this research, we review studies on automatic generation of examinations from question banks. From this review, we define the parameters necessary for a quality exam based on RBT. Finally, we propose a conceptual framework that applies machine learning algorithms to automatically generate a quality exam from an LMS question bank. We intend to do further research by developing a prototype based on the conceptual framework.