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Catching up with Method and Process Practice: An Industry-Informed Baseline for Researchers

Authors:

Abstract

Software development methods are usually not applied by the book. Companies are under pressure to continuously deploy software products that meet market needs and stakeholders’ requests. To implement efficient and effective development processes, companies utilize multiple frameworks, methods and practices, and combine these into hybrid methods. A common combination contains a rich management framework to organize and steer projects complemented with a number of smaller practices providing the development teams with tools to complete their tasks. In this paper, based on 732 data points collected through an international survey, we study the software development process use in practice. Our results show that 76.8% of the companies implement hybrid methods. Company size as well as the strategy in devising and evolving hybrid methods affect the suitability of the chosen process to reach company or project goals. Our findings show that companies that combine planned improvement programs with process evolution can increase their process’ suitability by up to 5%.
Catching up with Method and Process Practice:
An Industry-Informed Baseline for Researchers
Jil Kl¨
under, Regina Hebig, Paolo Tell, Marco Kuhrmann§, Joyce Nakatumba-Nabende, Rogardt Heldal,
Stephan Krusche∗∗, Masud Fazal-Baqaie††, Michael Felderer‡‡, Marcela Fabiana Genero Boccox, Steffen K¨
upper,
Sherlock A. Licorishxi , Gustavo L´
opezxii , Fergal Mc Cafferyxiii,¨
Ozden ¨
Ozcan Topxiii , Christian R. Prausexiv ,
Rafael Prikladnickixv , Eray T¨
uz¨
unxvi , Dietmar Pfahlxvii , Kurt Schneiderand Stephen G. MacDonellxviii
Leibniz University Hannover, Germany, Email: {jil.kluender, kurt.schneider}@inf.uni-hannover.de; Chalmers |University
of Gothenburg, Sweden, Email: {regina.hebig, heldal}@cse.gu.se, IT University Copenhagen, Denmark, Email: pate@itu.dk,
§Clausthal University of Technology, Germany, Email: {marco.kuhrmann, steffen.kuepper}@tu-clausthal.de, Makerere
University, Uganda, Email: jnakatumba@cis.mak.ac.ug, ∗∗Technical University of Munich, Germany,
Email: krusche@in.tum.de, ††Fraunhofer IEM, Germany, Email: masud.fazal-baqaie@iem.fraunhofer.de, ‡‡University of
Innsbruck, Austria, Email: michael.felderer@uibk.ac.at, xUniversity of Castilla-La Mancha, Spain,
Email: marcela.genero@uclm.es, xi University of Otago, New Zealand, Email: sherlock.licorish@otago.ac.nz, xiiUniversity of
Costa Rica, Costa Rica, Email: gustavo.lopez h@ucr.ac.cr, xiii Dundalk Institute of Technology & Lero, Ireland,
Email: {fergal.mccaffery, ozden.ozcantop}@dkit.ie, xiv DLR Space Administration, Germany, Email: christian.prause@dlr.de,
xv Pontif´
ıcia Universidade Cat´
olica do Rio Grande do Sul, Brazil, Email: rafael.prikladnicki@pucrs.br, xviBilkent University,
Turkey, Email: eraytuzun@cs.bilkent.edu.tr, xviiUniversity of Tartu, Estonia, Email: dietmar.pfahl@ut.ee,
xviii Auckland University of Technology, New Zealand, Email: smacdone@aut.ac.nz
Abstract—Software development methods are usually not ap-
plied by the book. Companies are under pressure to continuously
deploy software products that meet market needs and stakehold-
ers’ requests. To implement efficient and effective development
processes, companies utilize multiple frameworks, methods and
practices, and combine these into hybrid methods. A common
combination contains a rich management framework to organize
and steer projects complemented with a number of smaller
practices providing the development teams with tools to complete
their tasks. In this paper, based on 732 data points collected
through an international survey, we study the software develop-
ment process use in practice. Our results show that 76.8% of
the companies implement hybrid methods. Company size as well
as the strategy in devising and evolving hybrid methods affect
the suitability of the chosen process to reach company or project
goals. Our findings show that companies that combine planned
improvement programs with process evolution can increase their
process’ suitability by up to 5%.
Index Terms—software development, software process, hybrid
methods, survey research
I. INTRODUCTION
For decades, software companies, teams, and even individ-
ual developers have sought approaches that enable efficient and
effective software development. Since the 1970’s, numerous
processes have been proposed. The community started with
the Waterfall model [1], then the Spiral model [2], followed by
agile methods and lean development approaches [3]. Since the
early 2000s, few innovative software development approaches
were proposed, but several proposals for scaling agile methods,
e.g., SAFe or LeSS, were published. Meanwhile, an increasing
number of studies showing that modern software development
is neither purely “traditional” nor “agile” can be found re-
flecting that companies use processes comprised of various
development practices [4], [5].
Problem Statement: Research that focuses on agile meth-
ods and practices only cannot support practitioners who
are faced with the reality of hybrid development methods.
Similarly, the 100+ tailoring criteria [6], [7] for processes
established in the last decade seem to have no relevance for
practitioners who are devising hybrid methods and seeking im-
mediate and practical solutions to solve short-term problems.
Thus, process-related research has lost momentum as it is no
longer aligned with the concerns of practice.
Objective: In response to the situation above, our ob-
jective is to understand how and why practitioners devise
hybrid development methods. Our goal is to set a new baseline
for the next decade of evidence-based research on software
development approaches driven by practice.
Contribution: Based on an online survey comprising 732
data points we study the use of hybrid methods and the fac-
tors influencing the suitability of development approaches for
reaching goals. According to our results, 3/4of the companies
use a hybrid method, and company size and strategies to devise
hybrid methods influence the suitability of the approach to
achieve defined goals.
Context: This research is based on the HELENA study1,
which is a large-scale international survey in which 75 re-
searchers and practitioners from 25 countries participated. We
1HELENA: Hybrid dEveLopmENt Approaches in software systems devel-
opment, online: https://helenastudy.wordpress.com.
© IEEE. PREPRINT. This is the author's version of the work. It is posted here by permission of IEEE for your personal use.
Not for redistribution. The definitive version was published in the conference/workshop proceedings.
Stage 2b: Qualitative Analysis
Stage 0: Initial instrument development
(2015, 3 researchers, test: 15 subjects,
Germany)
Stage 1: Public instrument test + initial
data collection
- End of 2015: Extension of the research
team (11 researchers from Europe)
- End of 2015: Instrument revision
- Early 2016: Internal instrument test
-May-July 2016: Data collection Europe
- Result: 69 complete data points
- December 2016: Initial data analysis
and development of questions and
hypotheses for Stage 2
Stage 2: Final Instrument + Data Collection
- End of 2016: Extension of the research team
(75 researchers world wide)
- End of 2016: Instrument revision (scope:
precision of questions, topics, variables)
- Early 2017: Internal instrument test
(subjects: researchers not involved in rev.)
- Until May: Translation of the questionnaire
from English into German, Spanish and
Portuguese
-May-Nov. 2017: Data collection World wide
- Result: 1,467 total data points
(691 of these complete)
- December 2017: Start of data analysis
Stage 2: Data Analysis
- Data Cleaning and Reduction
- Qualitative Analysis
- Quantitative Analysis
Stage 2a: Quantitative Analysis
- Descriptive statistics
- Development/refinement of the
analysis model
- Hypothesis testing
April 27, 2018
-Formation of the
“coder” group
- Distribution of
coding template
Until May 2, 2018
- Initial codes of
all answers
Until May 7, 2018
- Analysis of used codes
- Harmonization of codes
- Distribution of round 2
coding template
Until May 21, 2018
- Second coding
with agreed codes
From May 21, 2018
Thematic analysis
of codes and
answers
Fig. 1. Overview of the research design.
give further details on the implementation of the HELENA
study in Section III-B.
Outline: The rest of this paper is organized as follows:
In Section II, we present related work. Section III presents our
research method. In Section IV, we present our results, which
are discussed in Section V. We conclude in Section VI.
II. RE LATE D WORK
The use of software development processes has been studied
since the 1970s, when the first ideas to structure software
development appeared [1], [2]. Since then, a growing number
of approaches emerged, ranging from traditional and rather
sequential models, to iterative and agile models. Various
combinations are used, forming hybrid methods.
In 2003, Cusumano et al. [8] surveyed 104 projects and
found many using and combining different development ap-
proaches. In an analysis of 12,000 projects, Jones [9] found
that both specific design methods and programming language
can lead to successful or troubled project outcome. Neill and
Laplante [10] found that approximately 35% of developers
used the classical Waterfall model. However, projects also
used incremental approaches, even within particular lifecy-
cle phases. In 2014, Tripp and Armstrong [11] investigated
the “most popular” agile methods and found XP, Scrum,
Dynamic Systems Development Method (DSDM), Crystal,
Feature Driven Development (FDD), and Lean development
among the top methods used. Only a few studies investigate
the development of processes over time. One perspective on
the use of agile methods is provided by Dingsøyr et al. [12].
They provided an overview of “a decade of agile software
development”, and motivate research towards a rigorous theo-
retical framework of agile software development, specifically,
on methods of relevance for industry. Such a perspective is
given by the VersionOne and the Swiss Agile surveys [13],
[14] that investigate the use of agile methods.
In 2011, West et al. [5] coined the term “Water-Scrum-
Fall” to describe the process pattern mostly applied in practice
at that time. Recent studies point to a trend towards using
such combined processes. Garousi et al. [15] as well as
Vijayasarathy and Butler [16] found that “classic” processes
like the Waterfall model are increasingly combined with agile
methods. Solinski and Petersen [17] found Scrum and XP to
be the most commonly adopted methods, with Waterfall/XP,
and Scrum/XP as the most common combinations. In 2017,
Kuhrmann et al. [4] generalized this concept, defining the
term “hybrid development methods” as “any combination of
agile and traditional (plan-driven or rich) approaches that an
organizational unit adopts and customizes to its own context
needs” [4]. They also confirmed that numerous development
processes are applied and combined with each other.
Available studies thus show a situation in which traditional
and agile approaches coexist and form the majority of practi-
cally used hybrid methods. In contrast, current literature on
software processes and their application in practice leaves
researchers and practitioners with an increasing amount of
research focusing only on agile methods. Traditional models
are vanishing from researchers’ focus. They only play a role
in process modeling, in domains with special requirements
(e.g., regulations and norms), or in discussions why certain
companies do not use agile methods (cf. [11], [18]).
Empirical data about general software process use, trends in
global regions, and detailed information about the combination
of processes is missing. To correctly portray the state of
practice, empirical data from industry is needed. This paper
fills this gap by providing a big picture of the use of hybrid
methods with respect to various development contexts (indus-
try sector, domain, company size) and different constraints
companies face.
III. RESEARCH MET HO D
We describe the overall research design following the steps
shown in Fig. 1by presenting the research objective and
research questions, followed by a description of the procedures
executed for the collection and analysis of data.
A. Research Objective and Research Questions
Our research objective is to understand why and how practi-
tioners use hybrid methods in practice. For this, we conducted
a large-scale international online survey to study (i) which
hybrid methods are practically used, (ii) how practitioners
devise such methods, and (iii) which strategies used to devise
such methods are beneficial. Emerging from the first stage of
our study (Fig. 1), the research questions are:
RQ1: Which software development approaches are used and
combined in practice? This question aims to determine the
state of practice to lay the foundation for our research. Specif-
ically, we study which methods, frameworks and practices are
used in practice and if they are combined.
RQ2: Which strategies are used to devise hybrid methods
in practice? This question aims at investigating why and
how hybrid methods are defined in practice, i.e., if specific
combinations are developed intentionally, if they evolve over
time, or if they were devised in response to specific situations.
Furthermore, we examine which goals are addressed by the
chosen development approach.
RQ3: Are there differences between the strategies used to de-
vise hybrid methods regarding gained benefits? When a hybrid
method is devised, this happens in response to an implicit or
explicit purpose, e.g., a need to improve communication. This
research question aims to analyze whether strategies to devise
hybrid development methods are comparable with regard to
gained benefits, i.e., that they equally allow practitioners to
devise a method that can fulfill the targeted purpose.
B. Instrument Development and Data Collection
We used the survey method [19] to collect our data. We
designed an online questionnaire to solicit data from practi-
tioners about the processes they use in their projects. The unit
of analysis was either a project or a software product.
1) Instrument Development: We used a multi-staged ap-
proach to develop the survey instrument. Initially, three re-
searchers developed the questionnaire and tested it with 15
German practitioners to evaluate suitability (Fig. 1, Stage 0).
Based on the feedback, a research team of eleven researchers
from across Europe revised the questionnaire. A public test of
the revised questionnaire, that included up to 25 questions,
was conducted in 2016 in Europe (Fig. 1, Stage 1). This
public test yielded 69 data points, which were analyzed and
used to initiate the next stage of the study [4]. In Stage 2,
the research team was extended, with 75 researchers from all
over the world now included. The revision of the questionnaire
for Stage 2 was concerned with improving structure and
scope, e.g., relevance and precision of the questions, value
ranges for variables, and relevance of the topics included. The
revised questionnaire was translated and made available in
English, German, Spanish, and Portuguese. Further details of
the instrument are presented in [20].
2) Instrument Structure: The final questionnaire consisted
of five parts (with number of questions): Demographics (10),
Process Use (13), Process Use and Standards (5), Experiences
(2) and Closing (8). In total, the questionnaire consisted of up
to 38 questions, depending on previously given answers [20].
3) Data Collection: The data collection period was May
to November 2017 following a convenience sampling strategy
[19]. The survey was promoted through personal contacts of
the 75 participating researchers, through posters at confer-
ences, and through posts to mailing lists, social media channels
(Twitter, Xing, LinkedIn), professional networks and websites
(ResearchGate and researchers’ (institution) home pages).
C. Data Analysis
As illustrated in Fig. 1, the data analysis approach applied to
the survey results included three main steps, which we present
in the following subsections.
1) Data Cleaning and Data Reduction: In total, the survey
yielded 1,467 answers, and 691 participants completed the
questionnaire. Hence, as a first step, we analyzed the two
datasets (partially and completely answered questionnaires)
and performed different analyses (descriptive statistics, two
researchers) to investigate the effects of using the partial or the
complete dataset. In the second step, two researchers reviewed
the data again in the context of the research questions and used
the results to develop a suggested dataset, which adds elements
from the partial dataset to the dataset containing the complete
answers. Finally, from the 1,467 answers, we selected 732
answers (49.9%) for inclusion in our data analysis. Each
answer forms a data point that consists of 206 variables (plus
meta data). The complete data set can be found in [20].
2) Quantitative Analysis: The quantitative analysis em-
ployed several instruments, e.g., descriptive statistics and
hypothesis testing. We summarize these instruments and we
describe how we handled the data to support these instruments.
a) Data Handling and Data Aggregation: At first, we
cleaned, aggregated and analyzed the data. Specifically, we
analyzed the data for NA and -9 values. While NA values
indicate that participants did not provide information for
optional questions, -9 values indicate that participants skipped
a question. Depending on the actual question, -9 values were
transformed into NA values, or the data point was excluded
from further analyses as we considered the question incom-
pletely answered. For example, if the question about the goals
addressed by a combination of methods (Fig. 2, PU12) was
answered, but the follow-up question for the suitability of the
combination regarding the goals set (Fig. 2, PU13) was not,
this data point was discarded. Furthermore, in the question on
company size (Fig. 2, D001), we integrated the category Micro
with the category Small, which results in a new category Micro
and Small (1–50 employees).
b) Development/Refinement of the Analysis Model: Fig-
ure 2shows the analysis model consisting of six questions in
the questionnaire, which we developed to provide a framework
for the (non-descriptive) quantitative analysis. In the rest of
the paper, we use short versions of the questions from Fig. 2
(together with the question ID to allow for a mapping). The
What are the overall goals that you aim to address with
your selection and combination of development
approaches?
+
To what degree did the combination of approaches
help you to achieve the your goals?
1. Yes, all projects of the company…
2. Each business unit has its own…
3. Each project can individually…
PU12+PU13
Does your company define a company-
wide standard process for software and
system development?
Do you intentionally deviate from
defined policies?
How were the combinations of
development frameworks, methods, and
practices in your company developed?
What is your company’s size in
equivalent full time employees?
1. Micro and Small
2. Medium
3. Large
4. Very Large
1. Planned as part of an SPI program
2. Evolved […] over time
3. Situation-specific
1. No
2. Yes, we intentionally deviate…
D001
PU01
PU11
PU07
H1
H2
H3
H4
Fig. 2. Analysis model for quantitative analysis. The model shows the six
questions (incl. question IDs), the value ranges and the linked hypotheses.
TABLE I
HYP OTH ESE S TO T EST T HE S UI TABI LI TY OF H YB RI D MET HO DS (RQ3).
Hypotheses
H10The suitability of a chosen development approach does not depend
on having a company-wide process.
H20The suitability of a chosen development approach does not depend
on deviating from defined policies.
H30The suitability of a chosen development approach does not depend
on the evolution of the combination.
H40The suitability of a chosen development approach does not depend
on the company size.
center of the analysis model (Fig. 2, left) is the combination
of the two questions PU12 and PU13 asking about the goals
set by combining development approaches in a specific way
and the suitability of this combination. The remaining four
questions were selected to study influence factors and depen-
dencies, e.g., does the company size (D001) or a specific way
of devising a hybrid method (PU07) influence the suitability.
The actual data analysis using our model was carried out in
two steps: (i) we explored the data on a per-question basis,
i.e., variables were analyzed in an isolated manner, and (ii)
we paired the different questions.
c) Hypothesis Testing: The final step in the quantitative
analysis (Fig. 1) was the hypothesis testing. Table Isumma-
rizes the hypotheses tested in the context of RQ3. To test the
hypotheses, we analyzed the data with statistical tests chosen
based on certain pre-conditions. Before the actual test, we
tested each variable for normality with the Shapiro-Wilk test2.
To test the hypotheses H1 to H4 we had to determine the
suitability of the respective hybrid method in relation to the
goals participants targeted while devising it. Participants could
choose from 18 goals, and for each selected goal g, participants
rated the suitability of the actual hybrid method on a 10-point
scale: suitg∈ {1,...,10},1g18. Since participants
could select a different number of goals, the suitability per
participant pwas standardized to abstract from the number
of individually selected goals: suitg(p)[0,1]. To apply the
analysis model, we calculated the total suitability for a given
participant and the overall suitability of a goal:
suittotal participant(p)=0.1·avg
g
{suitg(p, g)}
suittotal goal(g)=0.1·avg
p
{suitg(p, g)}
All variables of the analysis model (Fig. 2; PU01: company-
wide policies, PU11: deviation from these policies, PU07:
permutations of the different strategies to devise a hybrid
method, and D001: company size) were individually tested
against the suitability calculated for the different groups. For
this, we categorized the data and tested the respective means of
the suitability for significant differences on a per-variable basis
2The Shapiro-Wilk test is used to test if a sample comes from a normally
distributed population (null hypothesis).
using Pearson’s χ2test3and the Kruskal-Wallis test4. Finally,
we tested combinations of the variables using the Kruskal-
Wallis test. If evidence to reject the null-hypotheses was found,
effect sizes were calculated using ε2as suggested by Tomczak
and Tomczak [21]. For interpretation we apply commonly used
thresholds, inspired by Cohen’s interpretation of Pearson’s r
[22] and adapted the character of ε2: an effect size of 0.01
ε2<0.08 is considered small, 0.08 ε2<0.26 is considered
medium, and 0.26 ε2is considered large.
3) Qualitative Analysis: In the analysis it became clear that
deviating from defined policies might not lead to as much
benefit as other strategies. However, as deviation was reported
in many cases, we decided to conduct additional qualitative
analyses focusing on the reasons why developers intentionally
deviate from policies (optional free-text comment to PU11). In
addition, we investigated the free-text answers for reasons to
devise hybrid methods (PU06). Both analyses were performed
on the complete data set with 731 data points (one data point
was discarded due to missing answers).
The qualitative analysis was challenging due to the large
number of data points (267 out of 731 participants provided
answers for PU11 and 89 for PU06) as well as the language
diversity among the answers received (English, German, Span-
ish, and Portuguese). We addressed this by distributing the
analysis activity across a core team of three researchers and
an extended team of 12 additional researchers who focused
on coding the data. Together, we performed an analysis based
on coding, following the process shown in Fig. 1. The coding
process included five steps: (i) A core team of three researchers
prepared a coding template and organized the coding (taking
language skills/preferences into account) and the distribution
of the data such that two independent codings per data point
were performed. (ii) All 15 coders conducted the coding. In
total, this step yielded 123 codes for PU11 and 59 codes for
PU06—all codes in English. (iii) The core group analyzed the
codes and provided a harmonized set of 56 codes for PU11
and 50 codes for PU06 to the coding group. (iv) The coders
re-coded the data using the agreed codes. (v) The core group
performed a thematic analysis on the coded data. In total, nine
themes of reasons for deviation were named for PU11, and 38
additional reasons for devising hybrid methods were found
for PU06, including 16 reasons mentioned by more than one
participant.
IV. RES ULTS
After a characterization of the study population, we present
the results organized according to the research questions.
A. Study Population and Descriptive Statistics
As described in Section III-C1, 732 data points were in-
cluded in the data analysis. Answers were included from 46
countries, with 19 countries providing 10 or more answers and
3Pearson’s χ2tests whether two variables are independent (null hypothesis).
4The Kruskal-Wallis test is a non-parametrized test that can be applied
for comparing more than two groups. The test investigates if there are no
differences between the groups (null hypothesis).
TABLE II
OVE RVIE W OF C OMPA NY S IZE A ND PART IC IPAN TS ROL ES (N=732).
Micro/Small
Medium
Large
Very Large
no Info
Σ%
Developer 45 49 54 47 1 196 26.8
Project/Team Manager 32 42 33 36 – 143 19.5
Product Manager/Owner 24 13 14 18 – 69 9.4
Architect 15 15 19 14 – 63 8.6
Other 7 17 22 17 – 63 8.6
C-level Management (e.g., CIO, CTO) 26 12 8 4 50 6.8
Scrum Master/Agile Coach 10 10 8 21 – 49 6.7
Analyst/Requirements Engineer 12 11 11 4 2 40 5.5
Quality Manager 5 5 19 7 – 36 4.9
Tester – 6 7 1 14 1.9
Trainer 4 2 3 – 9 1.2
Σ180 182 198 169 3 732
%24.6 24.9 27.0 23.1 0.4 100
13 countries providing 20 or more answers. Most answers were
received from Germany (127), Brazil (80), Argentina (65),
Costa Rica (51), and Spain (50). The average time (median)
for completing the questionnaire was 23:36 minutes.
1) Respondent Profiles: Table II provides an overview of
the participants. The largest groups are developers (26.8%)
and project/team managers (19.5%). The 63 participants who
selected “other” as their role described themselves as func-
tional safety manager, data scientist, DevOps engineer, or
having multiple roles. Table II also shows the distribution of
the participants across the different company sizes, showing
companies of all sizes equally present in the result set. Three
participants did not provide information about the company
size. Additionally, 59.8% of the participants have 10 or more
years of experience in software and system development and
only 7.8% have two years or less of experience.
2) Project and Product Profiles: The unit of analysis in the
study at hand was a specific project or product. In total, 60.2%
of the participants classified their project as having one person
year or more of effort. Regarding the target domain, web
applications and services (26.8%) as well as financial services
(24.0%) are the most frequently mentioned. The remaining tar-
get domains include mobile applications (16.4%), automotive
software (10.4%), logistics (7.2%), and space systems (4.6%).
The 11.9% in the category “other target domains” named,
among others, agriculture, industry/production automation,
human resources, and stores/retail.
B. RQ1: Software Development Approaches
We are interested in capturing the state of practice in the
use of development frameworks, methods and practices, and
in analyzing whether these are combined with each other. Of
the 732 participants, 562 (76.8%) stated that they combine
different development approaches into a hybrid method. Two
questions asked about the use of 24 development frameworks
and methods, and 36 development practices, respectively. Par-
117
17
65
66
23
120
66
37
74
45
72
54
14
25
24
25
53
39
48
49
52
34
26
51
100
8
103
71
18
106
84
114
117
42
85
50
12
31
28
15
39
40
98
48
40
19
23
40
123
4
147
49
11
71
64
304
184
40
81
46
22
24
57
15
32
40
330
78
35
25
35
67
0% 20% 40% 60% 80% 100%
Classic Waterfall Process
Crystal Family
DevOps
Domain-driven Design
DSDM
Extreme Programming
Feature-driven Development
Iterative Development
Kanban
Large-scale Scrum
Lean Software Development
Mode l-dr iven Arch ite ctur e
Nexus
Personal Software Process
Phase/Stage-gate Model
PRINCE2
Rational Unified Process
Scaled Agile Framework
Scrum
SrcumBan
Spiral Model
SSADM
Team Software Process
V-shaped Process
We ra rely us e it We so metime s us e it We of ten/ alway s u se i t
Classic Waterfall Process
Crystal Family
DevOps
Domain-driven Design
DSDM
Extreme Programming
Feature-driven
Development
Iterative Development
Kanban
Large-scale Scrum
Lean Software
Development
Model-driven Architecture
Nexus
Personal Software
Process
Phase/Stage-gate Model
PRINCE2
Rational Unified Process
Scaled Agile Framework
Scrum
ScrumBan
Spiral Model
SSADM
Team Software Process
V-shaped Process
(n=340)
(n=29)
(n=315)
(n=186)
(n=52)
(n=297)
(n=214)
(n=455)
(n=375)
(n=127)
(n=238)
(n=150)
(n=48)
(n=80)
(n=109)
(n=55)
(n=124)
(n=119)
(n=476)
(n=175)
(n=127)
(n=78)
(n=84)
(n=158)
88
497
174
296
470
118
251
98
118
302
226
317
489
458
448
415
256
362
49
335
321
425
430
339
240
203
160
202
204
246
218
77
146
273
216
230
179
182
156
252
333
225
86
191
268
213
199
202
404
32
398
234
58
368
263
557
468
157
290
185
64
92
128
65
143
145
597
206
143
94
103
191
0% 20% 40% 60% 80% 100%
Classic Waterfall Process
Crystal Family
DevOps
Domain-driven Design
DSDM
Extreme Programming
Feature-driven Development
Iterative Development
Kanban
Large-scale Scrum
Lean Software Development
Mod el-d riven Arch ite ctur e
Nexus
Personal Software Process
Phase/Stage-gate Model
PRINCE2
Rational Unified Process
Scaled Agile Framework
Scrum
SrcumBan
Spiral Model
SSADM
Team Software Process
V-shaped Process
Don't know framework or if we use it We do n ot u se fra mewo rk We u se f rame wor k
139
18
80
79
25
147
78
51
98
60
92
65
19
30
33
30
62
51
57
55
60
42
30
63
118
9
126
88
22
132
104
139
138
50
101
60
18
34
31
17
46
46
112
62
43
22
26
49
147
5
192
67
11
89
81
367
232
47
97
60
27
28
64
18
35
48
428
89
40
30
47
79
0% 20% 40% 60% 80% 100%
Classic Waterfall Process
Crystal Family
DevOps
Domain-driven Design
DSDM
Extreme Programming
Feature-driven Development
Iterative Development
Kanban
Large-scale Scrum
Lean Software Development
Mode l-dr iven Arch ite ctur e
Nexus
Personal Software Process
Phase/Stage-gate Model
PRINCE2
Rational Unified Process
Scaled Agile Framework
Scrum
SrcumBan
Spiral Model
SSADM
Team Software Process
V-shaped Process
We ra rely us e it We some time s us e it We of ten/ alway s u se i t
117
17
65
66
23
120
66
37
74
45
72
54
14
25
24
25
53
39
48
49
52
34
26
51
100
8
103
71
18
106
84
114
117
42
85
50
12
31
28
15
39
40
98
48
40
19
23
40
123
4
147
49
11
71
64
304
184
40
81
46
22
24
57
15
32
40
330
78
35
25
35
67
0% 20% 40% 60% 80% 100%
Classic Waterfall Process
Crystal Family
DevOps
Domain-driven Design
DSDM
Extreme Programming
Feature-driven Development
Iterative Development
Kanban
Large-scale Scrum
Lean Software Development
Mode l-dr iven Arch ite ctur e
Nexus
Personal Software Process
Phase/Stage-gate Model
PRINCE2
Rational Unified Process
Scaled Agile Framework
Scrum
SrcumBan
Spiral Model
SSADM
Team Software Process
V-shaped Process
We ra rely us e it We s ometi mes u se i t We of ten/ alway s u se i t
Fig. 3. Overview of the knowledge and usage of frameworks and methods in
hybrid methods. The left part of the figure shows the breakdown for knowledge
and usage. The right part breaks down the “We use framework”-statements
into the usage frequency for the individual frameworks/methods.
TABLE III
MOST FREQUENTLY MENTIONED DEVELOPMENT FRAMEWORKS AND
METHODS WITHIN HYBRID METHODS (THRESHOLD 50%) AND S HA RE O F
CA SES T HAT RE PO RTED TO O FTE N OR A LWAYS USE T HAT FR AM EWO RK
(RE SPE CT IV E DATA FOR TH E WH OL E DATASET I N PAR ENT HE SE S).
Framework Rank % Use % Often/always
used when used
Scrum 1 (1) 84.7 (81.6) 69.3 (71.7)
Iterative Development 2 (2) 80.9 (76.1) 66.8 (65.9)
Kanban 3 (3) 66.7 (63.9) 49.1 (49.6)
Classic Waterfall 4 (4) 60.5 (55.2) 36.2 (36.4)
DevOps 5 (5) 56.0 (54.4) 46.7 (48.2)
Extreme Programming 6 (6) 52.8 (50.3) 23.9 (24.2)
ticipants stated whether they know the frameworks, methods
and practices as well as if they use a framework or practice,
and to what extent. An overview of the knowledge and use
of frameworks, methods, and practices in hybrid methods
is shown in Fig. 3(frameworks and methods) and Fig. 4
(practices). For both figures, we only consider answers of the
562 cases that reported using hybrid methods.
Finding 1: In total, 76.8% (562 out of 732) of the reported cases state to
use hybrid development methods.
Table III shows that Scrum, Iterative Development, Kan-
ban, the “classic” Waterfall model and DevOps are the most
frequently mentioned development methods or frameworks
within hybrid methods and also within the whole data set (in-
cluding non-hybrid development methods; [20]). Furthermore,
71
51
235
29
52
91
17
22
141
61
32
54
95
51
175
72
60
70
135
100
53
36
102
239
145
73
26
46
32
73
154
63
61
73
42
167
44
140
243
46
42
81
14
15
70
85
55
60
68
47
164
77
41
95
237
183
46
53
111
179
131
115
37
34
28
51
173
98
141
134
54
141
447
371
84
487
468
390
531
525
351
416
475
448
399
464
223
413
461
397
190
279
463
473
349
144
286
374
499
482
502
438
235
401
360
355
466
254
0% 20% 40% 60% 80% 100%
Architecture Specifications
Automated Code Generation
Automated Theorem Proving
Automated Unit Testing
Backlog Management
Burn-down Charts
Code Review
Coding Standards
Collective Code Ownership
Continuous Development
Continuous Integration
Daily Standup
Definition of Done/Ready
Design Reviews
Destructive Testing
Detailed Designs/Specifications
End-to-End Testing
Expert Estimation
Formal Estimation
Formal Secification
Iteration Planning
Iteration/Sprint Reviews
Limit Work-in-Progress
Mode l Ch ecki ng
On-Site Customer
Pair Programming
Prototyping
Refactoring
Release Planning
Retrospectives
Scrum-of-Scrums
Security Testing
Test-driven Development
Use Case Modeling
User Stories
Velocity-pased Planning
Do not know practice or if we use it We d o n ot u se p ract ice We use prac tic e
78
129
48
73
40
77
64
37
52
80
56
62
52
89
86
120
57
75
71
76
60
59
65
56
93
135
92
62
40
64
75
120
117
89
59
64
105
136
20
118
84
108
121
102
68
103
87
80
81
149
77
124
104
96
51
84
91
103
104
35
85
140
191
168
99
101
68
130
125
109
120
61
264
106
16
296
344
205
346
386
231
233
332
306
266
226
60
169
300
226
68
119
312
311
180
53
108
99
216
252
363
273
92
151
118
157
287
129
0% 20% 40% 60% 80% 100%
Architecture Specifications
Automated Code Generation
Automated Theorem Proving
Automated Unit Testing
Backlog Management
Burn-down Charts
Code Review
Coding Standards
Collective Code Ownership
Continuous Development
Continuous Integration
Daily Standup
Definition of Done/Ready
Design Reviews
Destructive Testing
Detailed Designs/Specifications
End-to-End Testing
Expert Estimation
Formal Estimation
Formal Secification
Iteration Planning
Iteration/Sprint Reviews
Limit Work-in-Progress
Mode l Ch ecki ng
On-Site Customer
Pair Programming
Prototyping
Refactoring
Release Planning
Retrospectives
Scrum-of-Scrums
Security Testing
Test-driven Development
Use Case Modeling
User Stories
Velocity-pased Planning
We ra rely us e pra ctic e We so meti mes u se i t We of ten/ alwa ys u se i t
Architecture Specifications (n=447)
Automated Code Generation (n=371)
Automated Theorem Proving (n=84)
Automated Unit Testing (n=487)
Backlog Management (n=468)
Burn-down Charts (n=390)
Code Review (n=531)
Coding Standards (n=525)
Collective Code Ownership (n=351)
Continuous Development (n=416)
Continuous Integration (n=475)
Daily Standup (n=448)
Definition of Ready/Done (n=399)
Design Reviews (n=464)
Destructive Testing (n=223)
Detailed Designs/
Design Specifications (n=413)
End-to-End Testing (n=461)
Expert/Team Estimation (n=397)
Formal Estimation (n=190)
Formal Specifications (n=279)
Iteration Planning (n=463)
Iteration/Sprint Reviews (n=473)
Limit Work-in-Progress (n=349)
Model Checking (n=144)
On-Site Customer (n=286)
Pair Programming (n=374)
Prototyping (n=499)
Refactoring (n=482)
Release Planning (n=502)
Retrospectives (n=438)
Scrum-of-Scrums (n=235)
Security Testing (n=401)
Test-driven Development (n=360)
Use Case Modeling (n=355)
User Stories (n=466)
Velocity-based Planning (n=254)
105
80
330
49
80
132
31
44
199
95
56
87
140
83
247
111
89
106
209
149
82
57
144
327
212
106
50
70
58
111
220
103
98
120
68
226
63
191
301
67
60
112
20
22
97
116
83
86
90
68
200
111
61
121
301
235
71
82
148
229
172
153
57
54
41
72
222
134
181
176
85
179
564
461
101
616
592
488
681
666
436
521
593
559
502
581
285
510
582
505
222
348
579
593
440
176
348
473
625
608
633
549
290
495
453
436
579
327
0% 20% 40% 60% 80% 100%
Architecture Specifications
Automated Code Generation
Automated Theorem Proving
Automated Unit Testing
Backlog Management
Burn-down Charts
Code Review
Coding Standards
Collective Code Ownership
Continuous Development
Continuous Integration
Daily Standup
Definition of Done/Ready
Design Reviews
Destructive Testing
Detailed Designs/Specifications
End-to-End Testing
Expert Estimation
Formal Estimation
Formal Secification
Iteration Planning
Iteration/Sprint Reviews
Limit Work-in-Progress
Mode l Ch ecki ng
On-Site Customer
Pair Programming
Prototyping
Refactoring
Release Planning
Retrospectives
Scrum-of-Scrums
Security Testing
Test-driven Development
Use Case Modeling
User Stories
Velocity-pased Planning
Do not know practice or if we use it We d o no t use pr acti ce We us e pr acti ce
96
158
56
94
50
93
85
51
65
97
70
73
59
114
110
144
72
85
88
104
75
68
87
65
110
166
125
85
55
78
88
141
143
113
71
81
142
166
24
147
111
130
159
124
88
127
107
92
94
180
101
151
133
120
60
101
113
117
122
49
106
176
234
200
116
111
77
158
151
135
136
73
326
137
21
375
431
265
437
491
283
297
416
394
349
287
74
215
377
300
74
143
391
408
231
62
132
131
266
323
462
360
125
196
159
188
372
173
0% 20% 40% 60% 80% 100%
Architecture Specifications
Automated Code Generation
Automated Theorem Proving
Automated Unit Testing
Backlog Management
Burn-down Charts
Code Review
Coding Standards
Collective Code Ownership
Continuous Development
Continuous Integration
Daily Standup
Definition of Done/Ready
Design Reviews
Destructive Testing
Detailed Designs/Specifications
End-to-End Testing
Expert Estimation
Formal Estimation
Formal Secification
Iteration Planning
Iteration/Sprint Reviews
Limit Work-in-Progress
Mode l Ch ecki ng
On-Site Customer
Pair Programming
Prototyping
Refactoring
Release Planning
Retrospectives
Scrum-of-Scrums
Security Testing
Test-driven Development
Use Case Modeling
User Stories
Velocity-pased Planning
We ra rely us e pra ctic e We s omet imes us e it We ofte n/a lway s use it
71
51
235
29
52
91
17
22
141
61
32
54
95
51
175
72
60
70
135
100
53
36
102
239
145
73
26
46
32
73
154
63
61
73
42
167
44
140
243
46
42
81
14
15
70
85
55
60
68
47
164
77
41
95
237
183
46
53
111
179
131
115
37
34
28
51
173
98
141
134
54
141
447
371
84
487
468
390
531
525
351
416
475
448
399
464
223
413
461
397
190
279
463
473
349
144
286
374
499
482
502
438
235
401
360
355
466
254
0% 20% 40% 60% 80% 100%
Architecture Specifications
Automated Code Generation
Automated Theorem Proving
Automated Unit Testing
Backlog Management
Burn-down Charts
Code Review
Coding Standards
Collective Code Ownership
Continuous Development
Continuous Integration
Daily Standup
Definition of Done/Ready
Design Reviews
Destructive Testing
Detailed Designs/Specifications
End-to-End Testing
Expert Estimation
Formal Estimation
Formal Secification
Iteration Planning
Iteration/Sprint Reviews
Limit Work-in-Progress
Mode l Ch ecki ng
On-Site Customer
Pair Programming
Prototyping
Refactoring
Release Planning
Retrospectives
Scrum-of-Scrums
Security Testing
Test-driven Development
Use Case Modeling
User Stories
Velocity-pased Planning
Do not know practice or if we use it We d o n ot u se p ract ice We us e pra ctic e
Fig. 4. Overview of the knowledge and usage of development practices in
hybrid methods. The left part of the figure shows the breakdown for knowledge
and usage. The right part breaks down the “We use practice”-statements into
the usage frequency for the individual practices.
Table III provides the rank in the category “We often/always
use” (column “% use”), which reads as follows: of the 84.7%
of participants stating that they use Scrum, 69.3% often or
always use Scrum. Each of the six frameworks and methods
in Table III is used by at least 50% of the participants reporting
to use hybrid methods. At the other end of the spectrum,
PRINCE2 (9.7%), DSDM (9.2%), and Nexus (8.5%), and
the Crystal Family (5.1%) received the smallest number of
mentions. Notable, the numbers do not change much when
considering the whole data set as also shown in Table III.
The practices (Fig. 4) draw a more diverse picture. Of the 36
practices provided in the questionnaire, 28 are used by more
than 50% of the participants who use hybrid methods. The
least used practices are Automated Theorem Proving (14.9%),
Model Checking (25.6%) and Formal Estimation (33.8%). The
most frequently mentioned development practices are Code
Reviews (94.5%) and Coding Standards (93.4%), followed by
Release Planning (89.3%), Prototyping (88.8%), Automated
Unit Testing (86.6%), and Refactoring (85.7%). Summarizing,
companies frequently use a variety of practices and, with a few
exceptions, most of the practices are widely used.
Finding 2: Companies combine frameworks, methods and practices to
form hybrid methods. Scrum, Iterative Development, Kanban, Waterfall
and DevOps are the most frequently used frameworks and methods.
C. RQ2: Strategies to Devise Hybrid Methods
In this section, we study why hybrid methods are used and
how they are devised using the analysis model from Fig. 2.
1) Policies and Deviation: First, we studied whether com-
panies have standard processes or policies defined (PU01)
and if the participants intentionally deviate from such policies
(PU11). Table IV shows that about half of the companies
have a company-wide standard process (45.8%), 19% of the
participants have standard processes defined at the business
unit level, and approx. 1/3of the participants (35.2%) indi-
vidually decide which process to follow. Yet, only 37.4% of
the participants state that they intentionally deviate from their
defined policies.
TABLE IV
COMPANY-WIDE POLICIES (PU01) AND DEVIATION (PU11).
Question/Answer Quantities
Does your company define a company-wide standard process for software
and system development? (PU01, n=732)
Yes, on company level 335 45.8%
Yes, on business unit level 139 19.0%
Each project can individually select the process 258 35.2%
Do you intentionally deviate from defined policies? (PU11, n=731)
Yes 274 37.5%
No 457 62.5%
2) Motivation for Devising Hybrid Methods: Approxi-
mately 3/4of the participants devise hybrid methods to run
their projects. Hence, we study reasons for devising such
methods. In the questionnaire, participants were asked two key
questions (Fig. 2; PU12 and PU13). Question PU12 provided
participants with 18 pre-defined goals (cf. Table VI) for which
they could state if these goals are drivers for the chosen
hybrid method. To ensure they did not miss a goal, PU12 was
complemented with an optional free-text field to collect further
goals. For each goal selected in question PU12, participants
were presented their selection in PU13 for the purpose of
evaluating if a specific goal was achieved through the hybrid
method (the analysis of the hybrid methods’ suitability is
presented in Section IV-C4).
In a nutshell, independent of whether or not respondents
deviated from defined company policies, the most frequently
named goals are: improved productivity,improved external
product quality,improved planning and estimation,improved
frequency of delivery to the customer and improved adapt-
ability and flexibility of the process to react to change. The
additional open question revealed some extra goals of which,
however, none represents a relevant addition.
Finding 3: The most popular goals addressed by companies are improved
productivity, improved external product quality, and improved planning
and estimation.
3) Strategies to Devise Hybrid Methods: Several strategies
are used to devise hybrid methods. Table Vshows that 37.8%
of the participants developed their hybrid method through a
choreographed software process improvement (SPI) initiative.
TABLE V
STR ATEGI ES T O DE VIS E (EVO LU TI ON OF )HYBRID METHODS (PU07).
Question/Answer Quantities
How were the combinations of development frameworks, methods, and
practices in your company developed? (PU07, n=543)
Planned as part of a SPI program 205 37.8%
Evolved as learning from past projects over time 426 78.5%
Situation-specific 46 8.5%
However, most hybrid methods evolve over time based on
learning from past projects (78.5%).
Finding 4: The most common way to devise hybrid development methods
is evolution, followed by planning as part of SPI initiatives.
4) Suitability of Devised Hybrid Methods: Table VI shows
the 18 pre-defined goals from which the participants could
choose (Fig. 2, PU12) and the suitability of the devised hybrid
methods for cases with an intentional deviation from a defined
company policy. The overall suitability is the average of all
suittotal goal(g)and results in 0.6575, i.e., for all cases, the
hybrid method was suitable to achieve the goals set to approx.
66%. Participants that do not deviate from a company policy
tend to perceive their hybrid methods slightly more suitable to
achieve their goals (67.47%, SD=16.48%, n=303) than those
who deviate (63.55%, SD=18.30%, n=228).
TABLE VI
OVERVIEW OF THE SET GOALS AND SUITABILITY OF HYBRID METHODS
TO AC HIE VE T HE M (N:NUM BE R OF PART IC IPAN TS T HAT HAVE S ET TH IS
GOA L; MED, M EA N, SD: THE SUITABILITY IN %).
Goal: Improved. . . n Med Mean SD
Productivity 165 70 61.2 24.03
External product quality 146 70 68.2 20.07
Planning and estimation 144 60 59.3 21.99
Frequency of delivery to customers 143 70 69.9 23.48
Adaptability and flexibility of the pro-
cess to react to change
131 80 73.1 21.49
Time to market 122 70 64.9 25.30
Client involvement 118 70 70.7 20.74
Internal artifact quality 112 70 67.1 21.42
Project monitoring and controlling 110 70 64.1 21.94
Knowledge transfer and learning 105 70 63.4 22.53
Employee satisfaction 102 70 67.3 23.68
Risk management 83 60 58.4 20.98
Reuse for project artifacts 79 60 59.8 20.00
Return-on-investment cycles 77 60 60.9 22.49
Maturity of the company 61 60 58.4 24.85
Staff education and development 59 70 62.4 24.09
Tool support 50 60 56.8 16.59
Ability of the company to develop
critical systems
40 60 59.8 26.84
Finding 5: Hybrid methods devised by practitioners are suitable to achieve
their goals with a probability of approx. 66%.
5) Reasons for Deviation: Our data suggests that deviating
from a defined company policy is disadvantageous even though
deviations are reported in many cases. To study reasons for
deviations, we conducted a thematic analysis on the optional
free-text answers that complement the question PU11. Follow-
ing the coding procedure (Section III-C3), we identified nine
themes (a threshold was set to five instances of a code). From
the 267 data points, 65 have been assigned to multiple codes
and, thus, have been assigned to multiple themes.
In total, 83 participants stated they have explicit goals for
deviating from the policies. Such goals often go hand in
hand with the motivation for creating hybrid methods such
as avoiding overhead (20) or compensating time pressure (18)
and resource constraints (7):
“We make appearances of following process and procedures, but really
just try to do what we can based on resource and skills constraints, and
because processes are defined by committees who don’t actually have
to deliver. This is the “How I have survived in this game for 30 years”
answer.[participant 1453]
Factors like flexibility (17), costs-benefit/efficiency (9), quality
(8), and speed (6) were also mentioned.
Accommodating context factors were stated by 75 partici-
pants. Such factors include project specific factors (45), differ-
ent or new technologies, domains, or tools (13), and different
teams (11). Accommodating the client, market or business as
a reason to deviate was mentioned by 72 participants, notably
due to partner/client requests (32). In 38 cases, participants
named shortcomings of the company’s standard process as
a reason to deviate as, for instance, the standard process is
described too abstract for direct implementation:
“Processes used case by case, the process is defined for the largest
possible project and everything is not applicable for smaller changes
and projects. Then selected parts can be removed or done differently.
[participant 2632]
Another nine participants stated that the standard process
was outdated or inappropriate, and five participants stated
that there is no standard process at all. Process improvement
was mentioned by 16 participants, e.g., for implementing a
continuous improvement approach and to build on experience.
Closely related, experimentation as a reason to deviate was
mentioned by 14 participants with the purpose of trying new
processes and methods. Another driver for deviation reported
by 12 participants is to create a fit between different processes,
organizations, or tools, whereas the need to align project
processes and client processes was highlighted:
“Our own processes and those of the customer had to be reconciled.
[participant 2960; translated from German]
Some reasons were named by only single participants, such
as organizational politics or deviation by mistake.
Finding 6: Deviation for a process is most commonly motivated by
explicit goals, context factors, the need to accommodate the client, market,
or business, and issues with the standard process of the company.
D. RQ3: Differences in Strategies to Devise Hybrid Methods
Companies use different development approaches in com-
bination as hybrid methods (Section IV-B) and use different
TABLE VII
RES ULTS O F THE KR USK AL -WAL LIS T ES T FO R H1 TO H4.
Id Results Decision
H10χ2= 2.78,df = 2, p = 0.2491 no statement
H20χ2= 5.5692,df = 1, p = 0.01828,ε2=0.013 reject
H30χ2= 10.93,df = 6, p = 0.09057 no statement
H40χ2= 18.83,df = 4, p = 0.0008487,ε2=0.0355 reject
TABLE VIII
AVERAGE SUITABILITY BY COMPANY SIZE (H4) AN D DE VIAT ION (H2).
Variable Value n suitgin %
Company Size Micro and Small 127 70.19734
Medium 131 66.91896
Large 149 63.59917
Very Large 124 62.71493
NA 1 46.66667
Intentional Deviation No 303 67.46700
Yes 228 63.54623
strategies to devise them (Table V). We analyze these strategies
with respect to their potential influence on the suitability of
using hybrid methods to achieve certain goals.
a) Isolated Test of Variables: Using our analysis model
(Section III-C2b), we explore the data by studying the vari-
ables from Fig. 2, i.e., company size, company-wide policies,
deviation from such policies and the way of devising hybrid
methods. For participants (n=562) stating that they use dif-
ferent processes in combination, we test the four variables in
relation to the suitability suitgof reaching the goals set through
the use of hybrid methods. As described in Section III-C2c,
for the variables of interest, we tested the normality of the
distribution of the suitability suitgwith the Shapiro-Wilk test
(W= 0.95714, p-value = 2.559 ×1011) and concluded that
the non-parametrized Kruskal-Wallis test should be used for
further analyses. Table VII summarizes the results of the tests
for the isolated influence of the company-wide process (H1),
the deviation (H2), the evolution (H3) and the company size
(H4) on the suitability of the process. Table VII also shows
that only the intentional deviation (H2) and company size
(H4) show significant differences in the treatment groups—
both show small, but non-negligible effect sizes. Table VIII
shows the average suitability to reach the goals in dependence
of the company size and the intentional deviation.
Finding 7: For projects in small companies, we found an 3.2% increased
chance to reach the goal compared to medium companies and 6.5%
compared to large companies.
Finding 8: For projects that intentionally deviate from the process, we
found an 3.9% decreased chance to reach the goals compared to projects
that do not intentionally deviate.
b) Combined Analysis of Variables: After the isolated
exploration of the variables of interest, we studied the combi-
nation of variables, i.e., are there effects on the suitability of
devising a hybrid method for companies that, for instance,
have a company-wide policy defined from which partici-
pants intentionally deviate. Furthermore, we analyzed specific
(combined) strategies to devise a hybrid method. To test the
potential effects of deviations from defined policies (Fig. 2;
PU01, PU11), we first confirmed with the Kruskal-Wallis test
that the combination of PU01 and PU11 is not significant
(χ2= 9.481,df = 5, p-value = 0.09135).
TABLE IX
AVERA GE SU ITAB IL IT Y BY ST RATE GY T O DEV IS E,I.E.THE EVOLUTION OF
A HY BRI D ME TH OD (PU07).
Variable Permutations n suitgin %
Strategies to devisea[0,0,0] 0 0
[0,0,1] 31 58.19790
[0,1,0] 294 65.65112 (select)
[0,1,1] 8 65.94692
[1,0,0] 81 63.76353 (select)
[1,0,1] 3 52.72727
[1,1,0] 111 70.06822 (select)
[1,1,1] 4 61.82540
aThe answers to question PU07 (Fig. 2) represent the permutations of the
multiple choice answer options [planned, evolved, situation-specific] with
1=selected and 0=not selected. For instance, [1,0,0] includes all participants
who only use a planned SPI-approach to devise a hybrid method.
TABLE X
SUITABILITY OF ADDRESSED GOALS BASED ON THE DIFFERENT
ST RATEG IE S TO D EVI SE (E VOL UT ION O F)A H YB RID M ET HO D (PU07).
[1,1,0] [0,1,0] [1,0,0]
n suitgn suitgn suitg
Group 111 0.7006822 294 0.6565112 81 0.6376353
Rest 421 0.6461631 238 0.6588072 451 0.6611130
χ2, df 6.755, 1 0.068546, 1 0.71172, 1
p-value 0.009348 0.7935 0.3989
Since the question for the evolution of the company’s
development approach is a multiple-choice one we built the
permutations and compared the groups with each other. Given
the differences in the samples, we decided to focus on the three
largest groups (Table IX), which were extracted and compared
to the rest of the sample (Table X). Among the groups, the
group [1,1,0], i.e., participants who devise their hybrid method
in a planned and evolutionary manner driven by experience
gathered in past projects, showed a significant difference with
a small, but non-negligible effect size (ε2=0.0127). The other
two groups did not show significant results.
Finding 9: Projects that devise hybrid processes applying both strategies
(planning as part of SPI and evolving it based on experience) have an
approx. 5% better chance to reach the goals set for devising the process.
V. DISCUSSION
We discuss our findings, research questions, threats to
validity and future directions of research.
A. Answering the Research Questions
To understand how practitioners use hybrid methods in prac-
tice, we formulated three research questions (Section III-A).
Based on our findings, we answer these as follows:
RQ1: Combining different development frameworks,
methods and practices is the state of practice. Almost all
methods and practices are used to form hybrid methods. The
methods most often used as ingredients in hybrid methods are
Scrum and Iterative Development.
RQ2: The most common strategy to devise hybrid meth-
ods is to evolve the process based on experience, followed by
planing a hybrid method as part of an SPI initiative. Explicitly
devising a hybrid method towards a specific project situation
is seldom. However, such situations are drivers for process
deviation.
RQ3: Several strategies are used to devise hybrid meth-
ods. The strategies are influenced by a number of context
factors. Our data shows that devising hybrid methods through a
planned SPI approach including experience from past projects
increases the probability of reaching the set goals, i.e., to
devise a meaningful method. Our data also shows that de-
viations from defined policies might decrease the probability
of reaching the goals. However, the rather small effect sizes
indicate that these results have to be interpreted with care.
While they indicate an impact, it is not clear how much
potential impact there is when improving the strategies to
devise hybrid methods. Hence, future studies should conduct
deeper analyses to further investigate this indication and show
under what conditions improvement can be reached.
Summary: Our findings show that devising hybrid meth-
ods helps practitioners reach set goals. However, even the
best strategies applied today are still suboptimal and are
not guaranteed to reach these goals. Furthermore, deviation
happens also for hybrid processes, yet, was observed to be
counterproductive in terms of achieving the goals.
Lessons Learned for Practitioners: When devising hybrid
methods, it seems to be the best strategy to first plan the hybrid
method and then evolve it based on experience. Whenever pos-
sible, it seems that it is better to mitigate process deviation. If
deviation happens, it is worthwhile to investigate the reasons.
For example, if developers perceive the standard process too
complex, it might help to revise the process together and to
plan an adaptation.
B. Limitations and Threats to Validity
We discuss threats to validity of this study following the
structure proposed by Wohlin et al. [23].
Construct Validity: The general threat to construct valid-
ity of questionnaire-based research is the risk that questions
are misunderstood by the participants. To mitigate this risk, we
designed the questionnaire with a team of multiple researchers,
involving internal and external reviews. We performed pre-
tests as described in Section III and, afterwards, conducted
a first survey with 69 subjects, which led to a revision of
the questionnaire. Potential misunderstandings due to language
issues were addressed by providing the questionnaire in four
languages (translated by native speakers). Due to these miti-
gation strategies, we are confident that risks are mitigated.
Another risk is that the participants do not reflect the desired
target population, since the link for participation was spread
via multiple networks and mailing lists. Thus, the survey could
have been answered by persons out of our target population
potentially introducing biases to the results. Based on the
specific knowledge required to answer the questionnaire and
the consistently meaningful free-text answers, we consider this
threat very small and, thus, mitigated.
Internal Validity: The preparation of the data and data-
cleaning can be considered a threat to the internal validity as
errors might have been introduced. Furthermore, the choice
and application of statistical tests can introduce errors. To
avoid this threat, all steps of the analysis have been performed
by two or more researchers and were later reviewed by
further researchers. Due to these review processes, we have
confidence that the method is reliable and reproducible. A risk
to the qualitative analysis could be incomplete assessments of
relevant themes and the incorrect summary of observations.
To minimize both, the qualitative analysis was conducted with
multiple researchers performing two rounds of coding. Data
was coded by multiple researchers. Finally, the summary of
the data was performed by a team of three researchers.
External Validity: The generalization of a single study to
all cases of software development is always a threat. However,
we reached a broad coverage of domains and participant roles
as well as an even distribution of company sizes. This allows
for making observations that are independent of these factors.
Concerning the generalizability of results across countries it
would have been interesting to have more data points from
Africa, Asia, and North America. Having few data points from
countries in these regions threatens the global generalizability
of our results. However, the data points that we have, e.g., from
Uganda, indicate that our results might be to some degree valid
for these regions. Future studies are needed to confirm this.
Conclusion Validity: For the statistical tests we worked
with a significance level of p0.05. The identified significant
results will have to be confirmed in future studies. However,
non-negligible effect sizes were observed, indicating that the
results are potentially relevant. The choice of the thresholds
can of course be discussed. Nonetheless, we contend that the
effects observed provide a baseline for future studies.
C. A Baseline for Future Research
Since our results show that hybrid methods are the current
state of practice, we suggest taking these findings as a new
baseline for future research on hybrid, flexible and adaptive
software development processes. In the following, we discuss
arising research challenges.
The strategies applied today are still some way from perfect
when it comes to devising hybrid methods. Therefore, the first
main future direction for research is to provide better strategies
to devise hybrid methods.
Studying the reasons for process deviation well help better
understand potential directions to mitigate the use of strategies
for process deviation. Among the most interesting observations
in our data is that goals for deviation are not necessarily
different from goals for the use of a hybrid method. Deviation
seems to be used where also a planned evolution could be
appropriate. Similarly, deviation to accommodate context fac-
tors or to improve the process could—in theory—also happen
in a more planned way as guidelines and even standards for
such initiatives are in place. It would be interesting to further
investigate why such a planned evolution is not happening
instead of the deviation.
Some cases of deviation are caused by external triggers and
requests that appear while the project is running. As a research
community, we need to help companies to develop strategies
to deal with such situations in a better, more efficient and
effective way, e.g., by helping practitioners document deviation
experiences from one project and providing better planned
alternative solutions for future projects that might become
subject to similar emergencies.
If deviation is frequently happen in a company’s projects,
it is worth reconsidering the standard process, as it might not
provide enough guidance or could be too complex. Processes
that aim to cover many (very) different project settings have
to be considered prone to deviation. Research should provide
straightforward guidelines or metrics to help practitioners
identify processes at risk for deviation, based on companies’
projects and structures.
Finally, we should research and develop strategies suitable
for use by practitioners when being confronted with the need
to integrate different processes or organizations.
VI. CONCLUSION
Companies usually do not apply development approaches
by the book. In fact, they often combine different develop-
ment frameworks, methods and practices. Among the most
frequently used frameworks and methods are Scrum and
Iterative Development. However, they are mostly combined
with other frameworks, methods and practices into so-called
hybrid development methods. While the chosen hybrid meth-
ods are generally suitable to reach the company’s goals with a
probability of 66%, project/product teams not deviating from
standard policies seem to be a bit more successful in achieving
their goals. The reasons for deviating from a process are,
among others, explicit goals, context factors as well as issues
with the standard process. However, the goals companies strive
for do not depend on the deviation from policies.
In a nutshell, devising hybrid methods helps practitioners
reach their goals. However, even the best strategies for devis-
ing hybrid methods are imperfect. Consequently, our findings
pose a new baseline for further research, which is necessary
to identify the best practices for devising hybrid development
methods.
ACK NOWLED GM EN TS
We thank all the study participants and the researchers
involved in the HELENA project for their great effort in col-
lecting data points. Dietmar Pfahl was supported by the group
grant IUT20-55 of the Estonian Research Council. Rafael Prik-
ladnicki is partially funded by FAPERGS (17/2551-0001/205-
4) and CNPq. Joyce Nakatumba-Nabende was supported by
the Sida/BRIGHT project 317 under the Makerere-Swedish
bilateral research programme 2015-2020. Fergal McCaffery
and ¨
Ozden ¨
Ozcan Top were supported by Science Foundation
Ireland grant 13/RC/2094 and co-funded under the European
Regional Development Fund through Lero, the Irish Software
Research Centre.
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