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

Artikel i vetenskaplig tidskrift
Författare Francisco Gomes de Oliveira Neto
Richard Torkar
Robert Feldt
Lucas Gren
Carlo A. Furia
Z. Huang
Publicerad i Journal of Systems and Software
Volym 156
Sidor 246-267
ISSN 01641212
Publiceringsår 2019
Publicerad vid Institutionen för data- och informationsteknik, Software Engineering (GU)
Sidor 246-267
Språk en
Länkar https://arxiv.org/pdf/1706.00933.pd...
Ämnesord Empirical software engineering, Practical significance, Semi-automated literature review, Statistical methods
Ämneskategorier Programvaruteknik

Sammanfattning

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 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.

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