TitleCommit-Aware Mutation Testing
Publication TypeConference Paper
Year of Publication2020
AuthorsMa W., Laurent T., Ojdanić M., Chekam T.T, Ventresque A., Papadakis M.
Conference Name2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)
Date PublishedSep.
Keywordschange relevant mutants, changed program behaviours, commit-aware mutation testing, commit-aware test assessment metric, commit-relevant mutants, commit-relevant test requirements, commits, committed changes, Conferences, continuous integration, Correlation, Java, Measurement, mutation testing, program testing, regression testing, Software maintenance, testing, user centred design
Abstract

In Continuous Integration, developers want to know how well they have tested their changes. Unfortunately, in these cases, the use of mutation testing is suboptimal since mutants affect the entire set of program behaviours and not the changed ones. Thus, the extent to which mutation testing can be used to test committed changes is questionable. To deal with this issue, we define commit-relevant mutants; a set of mutants that affect the changed program behaviours and represent the commit-relevant test requirements. We identify such mutants in a controlled way, and check their relationship with traditional mutation score (score based on the entire set of mutants or on the mutants located on the commits). We conduct experiments in both C and Java, using 83 commits, 2,253,610 mutants from 25 projects. Our findings reveal that there is a relatively weak correlation (Kendall/Pearson 0.15-0.4) between the sought (commit-relevant) and traditional mutation scores, indicating the need for a commit-aware test assessment metric. Our analysis also shows that traditional mutation is far from the envisioned case as it loses approximately 50%-60% of the commit-relevant mutants when analysing 5-25 mutants. More importantly, our results demonstrate that traditional mutation has approximately 30% lower chances of revealing commit-introducing faults than commit-aware mutation testing.

DOI10.1109/ICSME46990.2020.00045