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

TL;DR

This paper reviews the evolution of statistical analysis practices in empirical software engineering, highlighting trends, common methods, and proposing a workflow to improve rigor and reporting of practical significance.

Contribution

It provides a comprehensive analysis of statistical methods used in ESE and introduces a conceptual workflow with guidelines to enhance analysis quality and reporting.

Findings

Prevalent use of t-test and ANOVA in ESE

Increasing adoption of nonparametric tests and effect size measures

Current practices lack standardized reporting of practical significance

Abstract

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.