Evolution of statistical analysis in empirical software engineering research: Current state and steps forward
| Autor: | Robert Feldt, Francisco Gomes de Oliveira Neto, Carlo A. Furia, Ziwei Huang, Richard Torkar, Lucas Gren |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
media_common.quotation_subject 05 social sciences Empirical process (process control model) Nonparametric statistics 020207 software engineering Context (language use) 02 engineering and technology Data science Workflow Hardware and Architecture 0502 economics and business 0202 electrical engineering electronic engineering information engineering Conceptual model Statistical analysis State (computer science) Empirical evidence 050203 business & management Software Information Systems Statistical hypothesis testing media_common |
| Zdroj: | Journal of Systems and Software. 156:246-267 |
| ISSN: | 0164-1212 |
| DOI: | 10.1016/j.jss.2019.07.002 |
| Popis: | © 2019 Elsevier Inc. 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 analyse 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. |
| Databáze: | OpenAIRE |
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