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Evolution of statistical analysis in empirical software engineering research: Current state and steps forward
Chalmers, SWE.
Göteborgs universitet, SWE.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
Chalmers, SWE.
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2019 (English)In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 156, p. 246-267Article in journal (Refereed) Published
Abstract [en]

Software engineering research is evolving and papers are increasingly based on empirical data from a multitude of sources, using statistical tests to determine if and to what degree empirical evidence supports their hypotheses. To investigate the practices and trends of statistical analysis in empirical software engineering (ESE), this paper presents a review of a large pool of papers from top-ranked software engineering journals. First, we manually reviewed 161 papers and in the second phase of our method, we conducted a more extensive semi-automatic classification of papers spanning the years 2001–2015 and 5196 papers. Results from both review steps was used to: i) identify and analyse the predominant practices in ESE (e.g., using t-test or ANOVA), as well as relevant trends in usage of specific statistical methods (e.g., nonparametric tests and effect size measures) and, ii) develop a conceptual model for a statistical analysis workflow with suggestions on how to apply different statistical methods as well as guidelines to avoid pitfalls. Lastly, we confirm existing claims that current ESE practices lack a standard to report practical significance of results. We illustrate how practical significance can be discussed in terms of both the statistical analysis and in the practitioner's context. © 2019 Elsevier Inc.

Place, publisher, year, edition, pages
Elsevier Inc. , 2019. Vol. 156, p. 246-267
Keywords [en]
Empirical software engineering, Practical significance, Semi-automated literature review, Statistical methods, Analysis of variance (ANOVA), Automation, Software testing, Statistics, Conceptual model, Empirical data, Literature reviews, Non-parametric test, Semi-automatics, Software engineering journals, Engineering research
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Software Engineering
Identifiers
URN: urn:nbn:se:bth-18603DOI: 10.1016/j.jss.2019.07.002ISI: 000483658000016Scopus ID: 2-s2.0-85068745690OAI: oai:DiVA.org:bth-18603DiVA, id: diva2:1349791
Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-10-09Bibliographically approved

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Feldt, Robert

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