Evolution of statistical analysis in empirical software engineering research: Current state and steps forward
This work addresses the need for improved statistical rigor and standardization in empirical software engineering research, which is incremental as it builds on existing claims and provides actionable recommendations.
The paper reviewed statistical analysis practices in empirical software engineering research by analyzing 5,196 papers from 2001-2015, identifying predominant methods like t-tests and trends in usage, and developed a conceptual workflow with guidelines to address pitfalls and the lack of reporting on practical significance.
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 5,196 papers. Results from both review steps was used to: i) identify and analyze 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.