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

arXiv: https://arxiv.org/abs/1706.00933 · HTML: https://mlsanity.com/paper/1706.00933/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2017.

## Built on by (most cited first, 5 of 5)

- [A Survey on Data-driven Software Vulnerability Assessment and Prioritization](https://mlsanity.com/paper/2107.08364/) (2021)
- [Applications of statistical causal inference in software engineering](https://mlsanity.com/paper/2211.11482/) (2022)
- [A User Study on Explainable Online Reinforcement Learning for Adaptive Systems](https://mlsanity.com/paper/2307.04098/) (2023)
- [Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agent](https://mlsanity.com/paper/2512.14990/) (2025)
- [Ten Years of Teaching Empirical Software Engineering in the context of Energy-efficient Software](https://mlsanity.com/paper/2407.05689/) (2024)

## Abstract (arXiv)

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.