Citation: Kokol, P. Software Quality:
How Much Does It Matter? Electronics
2022,11, 2485. https://doi.org/
Academic Editor: Maysam Abbod
Received: 6 July 2022
Accepted: 2 August 2022
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Software Quality: How Much Does It Matter?
Faculty of Electrical Engineering and Computer Science, University of Maribor, SI-2000 Maribor, Slovenia;
Interconnected computers and software systems have become an indispensable part of
people’s lives in the period of digital transformation. Consequently, software quality research is
becoming more and more critical. There have been multiple attempts to synthesise knowledge
gained in software quality research; however, they were focused mainly on single aspects of software
quality and did not structure the knowledge holistically. To ﬁll this gap, we harvested software
quality publications indexed in the Scopus bibliographic database. We analysed them using synthetic
content analysis which is a triangulation of bibliometrics and content analysis. The search resulted in
15,468 publications. The performance bibliometric analysis showed that the production of research
publications relating to software quality is currently following an exponential growth trend and that
the software quality research community is growing. The most productive country was the United
States, followed by China. The synthetic content analysis revealed that the published knowledge
could be structured into six themes, the most important being the themes regarding software quality
improvement by enhancing software engineering, advanced software testing and improved defect
and fault prediction with machine learning and data mining.
software engineering; software quality; knowledge synthesis; bibliometrics; synthetic
The digital transformation caused interconnected computers and software systems
to become an indispensable part of our lives and the necessary toll for performing their
daily personal and business obligations and activities [
]. To fulﬁl global user needs for
storing, retrieving and processing information, knowledge and wisdom, those systems
have to be supported by quality software, which should function correctly and reliably, be
easy, safe and ﬁt to use, test, reuse and maintain, and ﬁnally to conform to stakeholders’
requirements. Therefore, software quality is not only one of the most important but also a
multidimensional attribute of computer software [2–4].
Consequently, there have been multiple attempts to synthesise knowledge gained in
software quality research; however, they were focused mainly on single aspects of software
quality, such as measurement [
], human-software interaction [
], empirical analysis of
Code smells and refactoring [
], design patterns [
]; quality models [
], human factors [
] and quality prediction [
] or to speciﬁc development approaches such as
Bibliometrics is another approach to synthesise knowledge [
]. Similarly to the above,
the only two bibliometrics studies concerned with software quality were focused on speciﬁc
aspects of software quality, one on defect prediction [
] and the other on code smells [
To close the gap regarding the lack of holistic knowledge synthesis studies of soft-
ware quality research, we performed a performance bibliometric analysis and synthetic
bibliometric mapping-based content analysis. We aimed to identify the most productive
countries and institutions, most proliﬁc source titles, publication production trends and hot
topics, and structure the research content into themes and trends.
Electronics 2022,11, 2485. https://doi.org/10.3390/electronics11162485 https://www.mdpi.com/journal/electronics
Electronics 2022,11, 2485 2 of 11
The choice to focus on software quality was motivated by the belief that software
quality assurance is a crucial factor in software development for a myriad of stakeholders
such as software developers, theoreticians, practitioners and of course, software users. The
study can help them gain new insights into the topic, deepen their knowledge or inform
them about the trends and essential themes in software quality research. To the best of
our knowledge, a similar quantitative and qualitative bibliometric study that provides
an overview of the current state and development of software quality research from its
beginnings to the present has not been conducted so far. Therefore, we eliminate this
shortcoming through the present study and ﬁll in the current gap.
2. Materials and Methods
Knowledge synthesis is an approach to deal with the explosive growth in research
literature production. Knowledge synthesis roots date back more than 120 years. However,
they became more popular in the 1960s [
] and even more commonly used toward the
end of the millennia with the emergence of the evidence-based paradigm [
analysis is one of the more popular knowledge synthesis methods used in qualitative and
quantitative research. Its main advantages are that it is content-sensitive, highly ﬂexible
and can be used to analyse many types of data in an inductive or deductive manner .
To enable the knowledge synthesis of several thousand or even ten thousand publica-
tions, Kokol et al. [
] triangulated descriptive bibliometrics, text mining and bibliometric
mapping and content analysis into Synthetic knowledge synthesis. In addition to semi-
automatic analysis of large corpora, the combination of the above approaches enables one
to combine quantitative and qualitative analysis of the content and production of research
publications. The qualitative part of the synthetic knowledge synthesis was performed
with the Algorithm presented below:
Harvest the research publications concerning software quality. The corpus of retrieved
publications represents the content to analyse and the output of Step 1.
Identify codes in the corpus using text mining and cluster them into an author’s
keyword landscape with bibliometric mapping. Authors’ keywords were selected as
codes since they most concisely present the content of a publication. The author’s
keyword landscape is the output from Step 2.
Condense author keywords with similar meanings into codes for every single cluster
and analyse the links between codes in individual clusters, and then map them into
categories which form the output from Step 3.
Analyse categories and name each cluster with an appropriate theme. The list of
themes is the ﬁnal output of the qualitative analysis.
In Step 1, the publications were harvested from Scopus (Elsevier, Amsterdam, The
Netherlands), the largest abstract and citation database of peer-reviewed literature. The
corpus was formed on 14 April 2022, for the whole period covered by Scopus. We used the
search string “quality software” OR “quality of software” OR “software quality” in informa-
tion source titles, abstracts, and keywords. The reliability of the search was assessed using
recall (fraction of the documents that are relevant to the query that is successfully retrieved)
information retrieval functions using 20 important software quality publications and ten
eminent authors (19 publications and all authors were retrieved). The publications and
authors were selected based on the discussions with colleagues concerned with software
engineering research. Using Scopus built-in functions, we exported the following metadata
to a CSV formatted corpus ﬁle: publication titles, authors’ afﬁliations’ details, source title,
publication type, publishing year, author keywords and funding data.
Bibliometric mapping in Step 2 has been performed using VOSViewer software version
1.6.16 (Leiden University, The Netherlands). VOSViewer uses text mining to recognise
various text entities in publications such as terms, keywords and country names. Next, it
employs the mapping technique, Visualisation of Similarities (VoS), based on the co-word
analysis. Landscapes are displayed in various ways to present different aspects of the
Electronics 2022,11, 2485 3 of 11
research publications’ content [
]. Steps 3 and 4 were performed using the traditional
content analysis process .
3. Results and Discussion
3.1. Descriptive Bibliometrics
The search resulted in 15,468 publications. Among them were 9457 conference papers,
4845 journal articles, 401 conference reviews, 294 book chapters, 283 reviews, 74 editorials,
63 books, 31 short papers and 20 other types of publications.
The ﬁrst paper indexed in Scopus was a conference paper titled Teaching software
development in a studio environment published in 1954 [
]. After that (Figure 1), the pro-
duction was low, not exceeding ﬁve publications per year till 1978 when the linear growth
trend began, followed by exponential growth starting in 2001. The peak productivity was
reached in 2019 with 1082 publications. The trend of the number of conference proceedings
and journal papers followed a similar pattern till 2001, then the trend split. The number of
journal papers followed a steady exponential trend reaching 400 journal articles in 2020,
while the number of conference papers had an explosive growth period from 2001 till
2007. After that, the number of conference papers remained steady till 2018 and peaked
in 2019 with 701 papers. According to the above trends, we can identify three milestones
in software quality research, namely: (1) The beginning of the seventies when the ﬁrst
software crisis emerged [
]; (2) the end of the seventies when software quality models and
measurements started to gain in importance [
]; and ﬁnally (3) in the beginning of the
new century with the advent of agile programming .
The dynamics of the softwarequality research expressed by the number of peer-reviewed publications.
Spatial Distribution and Productivity of Literature Production
The most proliﬁc countries in software quality research according to the number of
publications are presented in Table 1. Among them, India, China and Brazil belong among
the 10 best countries to outsource software development [
]. The United States, Germany,
India and the United Kingdom are the countries with the largest number of software
]. As expected, the most proliﬁc institutions (Table 2) are primarily located in
most proliﬁc countries.
Electronics 2022,11, 2485 4 of 11
Table 1. Most productive countries.
Country Number of Publications
United States 2917
United Kingdom 626
Table 2. Most productive institutions.
Institution Number of Publications
Florida Atlantic University 227
Beihang University 127
Amity University 99
Peking University 95
Carnegie Mellon University 87
Universidade de São Paulo 86
Technical University of Munich 83
École de Technologie Supérieure 80
Chinese Academy of Sciences 79
Fraunhofer Institute for Experimental Software
Engineering IESE 75
The most proliﬁc source titles (Table 3) are mainly conference and workshop pro-
ceedings. Most proliﬁc journals are prestigious top software engineering journals with
high SNIP 2020 impact factors. SNIP “measures contextual citation impact by weighting
citations based on the total number of citations in a subject ﬁeld, using Scopus data” .
Table 3. Most proliﬁc journals.
Source Title Number of Publications SNIP 2020
Lecture Notes in Computer Science Including Subseries Lecture
Notes in Artiﬁcial Intelligence And Lecture Notes
ACM International Conference Proceeding Series 394 0.296
Proceedings International Conference on Software Engineering
Communications In Computer and Information Science 226 0.32
Ceur Workshop Proceedings 221 0.345
Information And Software Technology 214 2.389
Software Quality Journal 191 1.388
Advances In Intelligent Systems and Computing 190 0.428
Journal Of Systems and Software 186 2.16
IEEE Software 152 1.934
According to the number of publications where a funding agency was mentioned in
the funding acknowledgement (Table 4), the most proliﬁc funding agency is the National
Natural Science Foundation of China (n= 318), followed by the US National Science Foun-
dation (n= 182) and European Commission (n= 132). Based on funding acknowledgements
analysis, only 21.5% of publications were sponsored, which is far less than for software
engineering in general (n= 32.3%) [
]. That might indicate that software quality research
is underfunded and did not yet reach the status of other software engineering disciplines.
Electronics 2022,11, 2485 5 of 11
Most productive funding institutions are located in China and Europe, and surprisingly
not as expected by other bibliometrics performance attributes, in the United States.
Table 4. Most proliﬁc funding agencies.
Funding Agency Number of Publications
National Natural Science Foundation of China 318
National Science Foundation (USA) 182
European Commission 132
Horizon 2020 Framework Programme (EU) 97
Conselho Nacional de Desenvolvimento
Cientíﬁco e Tecnológico (Brazil) 89
Natural Sciences and Engineering Research
Council of Canada 77
Coordenação de Aperfeiçoamento de Pessoal
de Nível Superior (Brazil) 76
National Key Research and Development
Program of China 66
European Regional Development Fund 65
Japan Society for the Promotion of Science 61
3.2. Qualitative Synthetic Knowledge Synthesis
3.2.1. Text Mining and Bibliometric Mapping
The 15,468 publications from the corpus were analysed using VOSviewer software
(Step 2 of the algorithm). Text mining identiﬁed 19,084 author keywords. All keywords
emerging in at least 40 papers (116 authors’ keywords) were included in the bibliometric
mapping analysis. The resulting author keywords landscape is shown in Figure 2.
Figure 2. Authors’ keywords cluster landscape.
Electronics 2022,11, 2485 6 of 11
3.2.2. Content Analysis
During the 3rd and 4th steps of the algorithm, author keywords were condensed into
34 codes. Those codes were next reduced into 16 categories. By analysing links between
categories and codes, we ﬁnally identiﬁed six themes of software quality presented below
and in Table 5:
•Software process improvement
: The most impactful research regarding the number
of citations was done around the beginning of the new millennia and was related to
software process maturity [
] and the importance of top management leadership,
management infrastructure and stakeholder participation [
]. Recent important re-
search is still concerned with process maturity, but CMM(I) is combined with DevOps
and agile approaches [35–37].
•Metric-based software maintenance
: Most cited papers related to this theme were
published in the period 1993–2010 and are concerned with object-oriented metrics to
predict maintainability [
], metrics-based refactoring [
], predicting faults [
code readability metrics [
]. Recent impactful papers deal with technical debt [
code smells and refactoring  and test smells .
•Software evolution and refactoring:
The most inﬂuential research was published in
the past decade and was concerned with the prediction of software evolution [45,46],
automatic detection of bad smells which can trigger refactoring [
], the association
of software defect and refactoring .
•Quality assurance in initial phases of software development life cycles:
ful papers were published after the end of millennia. The research was mainly focused
on how to assure software quality on the architectural level [
] and software
•Search-based software engineering for defect prediction and classiﬁcation:
search on this theme has become important in the last 15 years. The research mainly
used data mining and machine learning to predict software defects and failures using
static codes and other software documents [56–58].
•Software quality management and assurance with testing and inspections:
theme is the most established, with inﬂuential papers starting to be published around
40 years ago [
]. Recent research is concerned with regression testing [
tive mutation testing , metamorphic testing  and modern code reviews .
3.3. Hot Topics
To identify hot topics, we extended the methodology developed by Kokol et al. [
with the synthetic content analysis. We used this extended approach to compare the corpus
of publications published in 2018 and 2019 with the corpus of publications published in
2020 and 2021. All the new categories and themes emerging in 2020/21, and the categories
and themes emerging in 2018/19 mostly cited in 2020/21 were recognised as hot topics. In
that manner, we identiﬁed:
A new theme: Improving software development with the Integration of CMMI into
agile approaches .
Natural language processing of software documents to elicit high-quality re-
o Software quality attributes in agile environments .
o Software architectures for the internet of things .
o Software maintenance of blockchain-based software .
•Most cited categories in 2020/21:
o Detecting code smells with genetic algorithms .
o Use of Artiﬁcial intelligence in risk management .
o Use multi-criteria decision-making in software quality modelling .
Electronics 2022,11, 2485 7 of 11
Software quality research themes (numbers in parenthesis present the number of publications
in which an author keyword appeared).
Colour Codes Concepts/Categories Themes
Red (26 keywords))
Software engineering (641),
Software quality assurance
and management (330), Agile
software development (319),
Software development (276),
improvement (203), Software
process (109), Software
reuse (91), Project
Software quality assurance
with project and knowledge
process improvement with
agile approaches and CMMI;
software reuse with
production lines based on
software quality metrics;
Green (21 keywords)
Software testing (694),
Metrics (578), Reliability (108);
UML (80); Genetics
Metrics-based software testing
supported by genetic
algorithms; using and
metrics like reusability,
Blue (20 keywords)
Software quality (2685),
Empirical studies in software
Refactoring (226), Software
maintenance (259), Technical
depth and code smells (183),
Software evolution (130),)
Mining software repositories
to support empirical and
search software engineering;
software evolution with
refactoring based on code
smells; technical debts and
code smells in association
with software maintenance;
Software evolution and
Yellow (19 keywords)
Software architecture (253),
Software reliability (233),
(226), Software quality
models (214), Usability (108),
Quality metrics and
attributes (136), Quality
Quality attributes of software
requirements and architecture;
quality attributes of software
quality models and standards;
general quality metrics like
Quality assurance in initial
phases of software
development life cycles
Viollet (16 keywords)
Software metrics (568),
Machine learning and data
mining (479); Fault and defect
Use of software metrics and
data mining in defect
prediction and classiﬁcation;
engineering for defect
prediction and classiﬁcation
Light blue (14 keywords)
Quality (221), Software (204);
Quality management and
assurance (164); Testing (148);
Testing and inspection,
veriﬁcation and validation;
Software quality management
and assurance with testing
3.4. How Much Does the Software Quality Matter?
According to our synthesis, the volume, distribution and scope of quality research,
as well as the research community and the research literature production, are growing,
following the ever-growing importance of software in almost all human activities and
recognising the potentially catastrophic consequences of quality defects, despite the fact
that signiﬁcant part of software quality research is still presented at conferences a ro-
bust, and well-researched body of evidence is forming but is not yet achieved in yet
to be established core journals. Based on identiﬁed trends and best practices in overall
software quality, substantial research in software quality exists but would need to be tai-
Electronics 2022,11, 2485 8 of 11
lored and extended to the speciﬁc requirements of software quality sub-disciplines and
3.5. Strengths and Limitations
The study’s main strength is that it is the ﬁrst holistic content analysis of software
quality research. Another strength is that content analysis was performed using a novel
synthetic knowledge synthesis approach. One possible limitation is that the analysis was
limited to publications indexed in Scopus only; however, because Scopus covers the largest
and most complete set of information titles, we believe that we analysed most of the
important peer-reviewed publications.
Our bibliometric study showed that the production of research publications relating to
software quality is currently following an exponential growth trend and that the software
quality research community is growing. The synthetic content analysis revealed that the
published knowledge could be structured into six themes, most important being the themes
regarding software quality improvement by enhancing software engineering, advanced
software testing and improved defect and fault prediction with machine learning and data
mining. According to the analysis of the hot topics, it seems that future research will be
directed into developing and using a whole spectre of new artiﬁcial intelligence tools (not
just machine learning and data mining) and focusing on ensuring software quality in agile
The main contributions of or study are ﬁrst, a holistic and better understanding of
the software quality concept based on the triangulation of quantitative and qualitative
analysis, which provides a landscape, performance and spatial attributes of the software
quality research. Second, this study provides a paradigm for future studies in software
development and digital transformation topics research.
Funding: This research was funded by ARRS, grant number J7-2605.
Data Availability Statement: Not applicable.
Conﬂicts of Interest: The author declares no conﬂict of interest.
Kokol, P.; Vošner, H.B.; Kokol, M.; Završnik, J. The Quality of Digital Health Software: Should We Be Concerned? Digit. Health
2022,8, 20552076221109055. [CrossRef] [PubMed]
Tian, J. Software Quality Engineering: Testing, Quality Assurance and Quantiﬁable Improvement; Wiley India Pvt. Limited: New Delhi,
India, 2009; ISBN 978-81-265-0805-1.
Winkler, D.; Bifﬂ, S.; Mendez, D.; Wimmer, M.; Bergsmann, J. (Eds.) Software Quality: Future Perspectives on Software Engineering
Quality: 13th International Conference, SWQD 2021, Vienna, Austria, January 19–21, 2021, Proceedings; Lecture Notes in Business
Information Processing; Springer International Publishing: New York, NY, USA, 2021; ISBN 978-3-030-65853-3.
4. Gillies, A. Software Quality: Theory and Management, 3rd ed.; Lulu Press: Morrisville, NC, USA, 2011.
Suali, A.J.; Fauzi, S.S.M.; Nasir, M.H.N.M.; Sobri, W.A.W.M.; Raharjana, I.K. Software Quality Measurement in Software
Engineering Project: A Systematic Literature Review. J. Theor. Appl. Inf. Technol. 2019,97, 918–929.
Atoum, I. A Novel Framework for Measuring Software Quality-in-Use Based on Semantic Similarity and Sentiment Analysis of
Software Reviews. J. King Saud Univ. Comput. Inf. Sci. 2020,32, 113–125. [CrossRef]
Lacerda, G.; Petrillo, F.; Pimenta, M.; Guéhéneuc, Y.G. Code Smells and Refactoring: A Tertiary Systematic Review of Challenges
and Observations. J. Syst. Softw. 2020,167, 110610. [CrossRef]
Wedyan, F.; Abufakher, S. Impact of Design Patterns on Software Quality: A Systematic Literature Review. IET Softw.
14, 1–17. [CrossRef]
Saini, G.L.; Panwar, D.; Kumar, S.; Singh, V. A Systematic Literature Review and Comparative Study of Different Software Quality
Models. J. Discret. Math. Sci. Cryptogr. 2020,23, 585–593. [CrossRef]
Guveyi, E.; Aktas, M.S.; Kalipsiz, O. Human Factor on Software Quality: A Systematic Literature Review. In International
Conference on Computational Science and Its Applications; Springer: Cham, Switzerland, 2020; pp. 918–930. [CrossRef]
Khatibsyarbini, M.; Isa, M.A.; Jawawi, D.N.A.; Tumeng, R. Test Case Prioritization Approaches in Regression Testing: A
Systematic Literature Review. Inf. Softw. Technol. 2018,93, 74–93. [CrossRef]
Electronics 2022,11, 2485 9 of 11
12. Rathore, S.S.; Kumar, S. A Study on Software Fault Prediction Techniques. Artif. Intell. Rev. 2019,51, 255–327. [CrossRef]
Cowlessur, S.K.; Pattnaik, S.; Pattanayak, B.K. A Review of Machine Learning Techniques for Software Quality Prediction. Adv.
Intell. Syst. Comput. 2020,1089, 537–549. [CrossRef]
Kupiainen, E.; Mäntylä, M.V.; Itkonen, J. Using Metrics in Agile and Lean Software Development—A Systematic Literature
Review of Industrial Studies. Inf. Softw. Technol. 2015,62, 143–163. [CrossRef]
Van Raan, A. Measuring Science: Basic Principles and Application of Advanced Bibliometrics. In Springer Handbook of Science
and Technology Indicators; Glänzel, W., Moed, H.F., Schmoch, U., Thelwall, M., Eds.; Springer Handbooks; Springer International
Publishing: Cham, Switzerland, 2019; pp. 237–280. ISBN 978-3-030-02511-3.
Pachouly, J.; Ahirrao, S.; Kotecha, K. A Bibliometric Survey on the Reliable Software Delivery Using Predictive Analysis. Libr.
Philos. Pract. 2020,2020, 1–27.
17. Kokol, P.; Kokol, M.; Zagoranski, S. Code Smells: A Synthetic Narrative Review. arXiv 2021, arXiv:2103.01088.
Chalmers, I.; Hedges, L.V.; Cooper, H. A Brief History of Research Synthesis. Eval Health Prof
,25, 12–37. [CrossRef]
19. Tricco, A.C.; Tetzlaff, J.; Moher, D. The Art and Science of Knowledge Synthesis. J. Clin. Epidemiol. 2011,64, 11–20. [CrossRef]
Whittemore, R.; Chao, A.; Jang, M.; Minges, K.E.; Park, C. Methods for Knowledge Synthesis: An Overview. Heart Lung
43, 453–461. [CrossRef]
Kyngäs, H.; Mikkonen, K.; Kääriäinen, M. The Application of Content Analysis in Nursing Science Research; Springer Nature:
Berlin/Heidelberg, Germany, 2020. [CrossRef]
Kokol, P.; Kokol, M.; Zagoranski, S. Machine Learning on Small Size Samples: A Synthetic Knowledge Synthesis. Sci. Prog.
105, 00368504211029777. [CrossRef]
Van Eck, N.J.; Waltman, L. Software Survey: VOSviewer, a Computer Program for Bibliometric Mapping. Scientometrics
84, 523–538. [CrossRef]
Tomayko, J.E. Teaching Software Development in a Studio Environment. In Proceedings of the SIGCSE 1991, San Antonio, TX,
USA, 7–8 March 1991; pp. 300–303.
25. Dijkstra, E.W. The Humble Programmer. Commun. ACM 1972,15, 859–866. [CrossRef]
Cavano, J.P.; McCall, J.A. A Framework for the Measurement of Software Quality. SIGMETRICS Perform. Eval. Rev.
7, 133–139. [CrossRef]
Dingsøyr, T.; Nerur, S.; Balijepally, V.; Moe, N.B. A Decade of Agile Methodologies: Towards Explaining Agile Software
Development. J. Syst. Softw. 2012,85, 1213–1221. [CrossRef]
10 Best Countries to Outsource Software Development, Based on Data. 2019. Available online: https://www.codeinwp.com/
blog/best-countries-to-outsource-software-development/ (accessed on 25 July 2022).
Qubit Labs. How Many Programmers Are There in the World and in the US? 2022. Available online: https://qubit-labs.
North%20America (accessed on 25 July 2022).
Journal Metrics in Scopus: Source Normalized Impact per Paper (SNIP)|Elsevier Scopus Blog. Available online: https://blog.
scopus.com/posts/journal-metrics-in-scopus- source-normalized-impact-per-paper-snip (accessed on 25 July 2022).
Kokol, P. Funded and Non-Funded Research Literature in Software Engineering in Relation to Country Determinants. COLLNET
J. Scientometr. Inf. Manag. 2019,13, 103–109. [CrossRef]
Harter, D.E.; Krishnan, M.S.; Slaughter, S.A. Effects of Process Maturity on Quality, Cycle Time, and Effort in Software Product
Development. Manag. Sci. 2000,46, 451–466. [CrossRef]
Herbsleb, J.; Zubrow, D.; Goldenson, D.; Hayes, W.; Paulk, M. Software Quality and the Capability Maturity Model. Commun.
ACM 1997,40, 30–40. [CrossRef]
Ravichandran, T.; Rai, A. Quality Management in Systems Development: An Organizational System Perspective. MIS Q. Manag.
Inf. Syst. 2000,24, 381–410. [CrossRef]
Alqadri, Y.; Budiardjo, E.K.; Ferdinansyah, A.; Rokhman, M.F. The CMMI-Dev Implementation Factors for Software Quality
Improvement: A Case of XYZ Corporation. In Proceedings of the 2020 2nd Asia Paciﬁc Information Technology Conference, Bali
Island, Indonesia, 17–19 January 2020; pp. 34–40.
Ferdinansyah, A.; Purwandari, B. Challenges in Combining Agile Development and CMMI: A Systematic Literature Review. In
Proceedings of the ICSCA 2021: 2021 10th International Conference on Software and Computer Applications, Kuala Lumpur,
Malaysia, 23–26 February 2021; pp. 63–69.
Raﬁ, S.; Yu, W.; Akbar, M.A.; Mahmood, S.; Alsanad, A.; Gumaei, A. Readiness Model for DevOps Implementation in Software
Organizations. J. Softw. Evol. Process 2021,33, e2323. [CrossRef]
38. Li, W.; Henry, S. Object-Oriented Metrics That Predict Maintainability. J. Syst. Softw. 1993,23, 111–122. [CrossRef]
Simon, F.; Steinbrückner, F.; Lewerentz, C. Metrics Based Refactoring. In Proceedings of the Fifth European Conference on
Software Maintenance and Reengineering, Lisbon, Portugal, 14–16 March 2001; pp. 30–38.
Olague, H.M.; Etzkorn, L.H.; Gholston, S.; Quattlebaum, S. Empirical Validation of Three Software Metrics Suites to Predict
Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes. IEEE
Trans. Softw. Eng. 2007,33, 402–419. [CrossRef]
41. Buse, R.P.L.; Weimer, W.R. Learning a Metric for Code Readability. IEEE Trans. Softw. Eng. 2010,36, 546–558. [CrossRef]
Electronics 2022,11, 2485 10 of 11
Tsoukalas, D.; Kehagias, D.; Siavvas, M.; Chatzigeorgiou, A. Technical Debt Forecasting: An Empirical Study on Open-Source
Repositories. J. Syst. Softw. 2020,170, 110777. [CrossRef]
Agnihotri, M.; Chug, A. A Systematic Literature Survey of Software Metrics, Code Smells and Refactoring Techniques. J. Inf.
Processing Syst. 2020,16, 915–934. [CrossRef]
Kim, D.J.; Chen, T.-H.; Yang, J. The Secret Life of Test Smells—An Empirical Study on Test Smell Evolution and Maintenance.
Empir. Softw. Eng. 2021,26, 1–47. [CrossRef]
Bhattacharya, P.; Iliofotou, M.; Neamtiu, I.; Faloutsos, M. Graph-Based Analysis and Prediction for Software Evolution. In
Proceedings of the ICSE ‘12: 34th International Conference on Software Engineering, Zurich, Switzerland, 2–9 June 2012;
Wyrich, M.; Bogner, J. Towards an Autonomous Bot for Automatic Source Code Refactoring. In Proceedings of the 2019
IEEE/ACM 1st International Workshop on Bots in Software Engineering (BotSE), Montreal, QC, Canada, 28 May 2019; pp. 24–28.
Fontana, F.A.; Braione, P.; Zanoni, M. Automatic Detection of Bad Smells in Code: An Experimental Assessment. J. Object Technol.
2012,11, 1–38. [CrossRef]
Ouni, A.; Kessentini, M.; Sahraoui, H.; Inoue, K.; Deb, K. Multi-Criteria Code Refactoring Using Search-Based Software
Engineering: An Industrial Case Study. ACM Trans. Softw. Eng. Methodol. 2016,25, 1–53. [CrossRef]
Bavota, G.; De Lucia, A.; Marcus, A.; Oliveto, R. Automating Extract Class Refactoring: An Improved Method and Its Evaluation.
Empir. Softw. Eng. 2014,19, 1617–1664. [CrossRef]
Ratzinger, J.; Sigmund, T.; Gall, H.C. On the Relation of Refactoring and Software Defects. In Proceedings of the 2008 International
Working Conference on Mining Software Repositories, MSR 2008, Leipzig, Germany, 10–11 May 2008; pp. 35–38.
51. Folmer, E.; Bosch, J. Architecting for Usability: A Survey. J. Syst. Softw. 2004,70, 61–78. [CrossRef]
Wang, W.-L.; Pan, D.; Chen, M.-H. Architecture-Based Software Reliability Modeling. J. Syst. Softw.
,79, 132–146. [CrossRef]
Mellado, D.; Fernández-Medina, E.; Piattini, M. A Common Criteria Based Security Requirements Engineering Process for the
Development of Secure Information Systems. Comput. Stand. Interfaces 2007,29, 244–253. [CrossRef]
Sedeño, J.; Schön, E.-M.; Torrecilla-Salinas, C.; Thomaschewski, J.; Escalona, M.J.; Mejias, M. Modelling Agile Requirements Using
Context-Based Persona Stories. In Proceedings of the WEBIST 2017: 13th International Conference on Web Information Systems
and Technologies, Porto, Portugal, 25–27 April 2017; pp. 196–203.
Shull, F.; Rus, I.; Basili, V. How Perspective-Based Reading Can Improve Requirements Inspections. Computer
Menzies, T.; Greenwald, J.; Frank, A. Data Mining Static Code Attributes to Learn Defect Predictors. IEEE Trans. Softw. Eng.
33, 2–13. [CrossRef]
Lessmann, S.; Baesens, B.; Mues, C.; Pietsch, S. Benchmarking Classiﬁcation Models for Software Defect Prediction: A Proposed
Framework and Novel Findings. IEEE Trans. Softw. Eng. 2008,34, 485–496. [CrossRef]
Song, Q.; Shepperd, M.; Cartwright, M.; Mair, C. Software Defect Association Mining and Defect Correction Effort Prediction.
IEEE Trans. Softw. Eng. 2006,32, 69–82. [CrossRef]
Boehm, B.W.; Brown, J.R.; Lipow, M. Quantitative Evaluation of Software Quality. In Proceedings of the 2nd International
Conference on Software Engineering, San Francisco, CA, USA, 13–15 October 1976; pp. 592–605.
60. Fagan, M.E. Advances in Software Inspections. IEEE Trans. Softw. Eng. 1986,SE-12, 744–751. [CrossRef]
61. McCabe, T.J.; Butler, C.W. Design Complexity Measurement and Testing. Commun. ACM 1989,32, 1415–1425. [CrossRef]
Zhang, J.; Zhang, L.; Harman, M.; Hao, D.; Jia, Y.; Zhang, L. Predictive Mutation Testing. IEEE Trans. Softw. Eng.
Segura, S.; Towey, D.; Zhou, Z.Q.; Chen, T.Y. Metamorphic Testing: Testing the Untestable. IEEE Softw.
,37, 46–53. [CrossRef]
Uchoa, A.; Barbosa, C.; Oizumi, W.; Blenilio, P.; Lima, R.; Garcia, A.; Bezerra, C. How Does Modern Code Review Impact Software
Design Degradation? An In-Depth Empirical Study. In Proceedings of the 2020 IEEE International Conference on Software
Maintenance and Evolution (ICSME), Adelaide, Australia, 28 September–2 October 2020; pp. 511–522.
65. Kokol, P.; Završnik, J.; Blažun Vošner, H. Bibliographic-Based Identiﬁcation of Hot Future Research Topics: An Opportunity for
Hospital Librarianship. J. Hosp. Librariansh. 2018,18, 1–8. [CrossRef]
Amer, S.K.; Badr, N.; Hamad, A. Combining CMMI Speciﬁc Practices with Scrum Model to Address Shortcomings in Process
Maturity. Adv. Intell. Syst. Comput. 2020,921, 898–907. [CrossRef]
Ahmad, A.; Feng, C.; Khan, M.; Khan, A.; Ullah, A.; Nazir, S.; Tahir, A. A Systematic Literature Review on Using Machine
Learning Algorithms for Software Requirements Identiﬁcation on Stack Overﬂow. Secur. Commun. Netw.
Oriol, M.; Martínez-Fernández, S.; Behutiye, W.; Farré, C.; Kozik, R.; Seppänen, P.; Vollmer, A.M.; Rodríguez, P.; Franch, X.;
Aaramaa, S.; et al. Data-Driven and Tool-Supported Elicitation of Quality Requirements in Agile Companies. Softw. Qual. J.
28, 931–963. [CrossRef]
Minerva, R.; Lee, G.M.; Crespi, N. Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural
Models. Proc. IEEE 2020,108, 1785–1824. [CrossRef]
70. Liu, X.L.; Wang, W.M.; Guo, H.; Barenji, A.V.; Li, Z.; Huang, G.Q. Industrial Blockchain Based Framework for Product Lifecycle
Management in Industry 4.0. Robot. Comput.-Integr. Manuf. 2020,63, 101897. [CrossRef]
Electronics 2022,11, 2485 11 of 11
Caram, F.L.; Rodrigues, B.R.D.O.; Campanelli, A.S.; Parreiras, F.S. Machine Learning Techniques for Code Smells Detection: A
Systematic Mapping Study. Int. J. Softw. Eng. Knowl. Eng. 2019,29, 285–316. [CrossRef]
Winkle, T.; Erbsmehl, C.; Bengler, K. Area-Wide Real-World Test Scenarios of Poor Visibility for Safe Development of Automated
Vehicles. Eur. Transp. Res. Rev. 2018,10, 32. [CrossRef]
Sun, G.; Guan, X.; Yi, X.; Zhou, Z. An Innovative TOPSIS Approach Based on Hesitant Fuzzy Correlation Coefﬁcient and Its
Applications. Appl. Soft Comput. J. 2018,68, 249–267. [CrossRef]