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Software engineering research is evolving and papers are increasingly basedon empirical data from a multitude of sources, using statistical tests todetermine if and to what degree empirical evidence supports their hypotheses.To investigate the practices and trends of statistical analysis in empiricalsoftware engineering (ESE), this paper presents a review of a large pool ofpapers from top-ranked software engineering journals. First, we manuallyreviewed 161 papers and in the second phase of our method, we conducted a moreextensive semi-automatic classification of papers spanning the years 2001--2015and 5,196 papers. Results from both review steps was used to: i) identify andanalyze the predominant practices in ESE (e.g., using t-test or ANOVA), as wellas relevant trends in usage of specific statistical methods (e.g.,nonparametric tests and effect size measures) and, ii) develop a conceptualmodel for a statistical analysis workflow with suggestions on how to applydifferent statistical methods as well as guidelines to avoid pitfalls. Lastly,we confirm existing claims that current ESE practices lack a standard to reportpractical significance of results. We illustrate how practical significance canbe discussed in terms of both the statistical analysis and in thepractitioner's context.

Evolution of statistical analysis in empirical software engineering research: Current state and steps forward