Ma, W;
Chekam, TT;
Papadakis, M;
Harman, M;
(2021)
MuDelta: Delta-Oriented Mutation Testing at Commit Time.
In:
2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE).
(pp. pp. 897-909).
IEEE: Madrid, ES.
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Abstract
To effectively test program changes using mutation testing, one needs to use mutants that are relevant to the altered program behaviours. In view of this, we introduce MuDelta, an approach that identifies commit-relevant mutants; mutants that affect and are affected by the changed program behaviours. Our approach uses machine learning applied on a combined scheme of graph and vector-based representations of static code features. Our results, from 50 commits in 21 Coreutils programs, demonstrate a strong prediction ability of our approach; yielding 0.80 (ROC) and 0.50 (PR Curve) AUC values with 0.63 and 0.32 precision and recall values. These predictions are significantly higher than random guesses, 0.20 (PR-Curve) AUC, 0.21 and 0.21 precision and recall, and subsequently lead to strong relevant tests that kill 45%more relevant mutants than randomly sampled mutants (either sampled from those residing on the changed component(s) or from the changed lines). Our results also show that MuDelta selects mutants with 27% higher fault revealing ability in fault introducing commits. Taken together, our results corroborate the conclusion that commit-based mutation testing is suitable and promising for evolving software.
| Type: | Proceedings paper |
|---|---|
| Title: | MuDelta: Delta-Oriented Mutation Testing at Commit Time |
| Event: | 43rd IEEE/ACM International Conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP) / 43rd ACM/IEEE International Conference on Software Engineering - New Ideas and Emerging Results (ICSE-NIER) |
| Location: | ELECTR NETWORK |
| Dates: | 25 May 2021 - 28 May 2021 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICSE43902.2021.00086 |
| Publisher version: | https://doi.org/10.1109/ICSE43902.2021.00086 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Computer Science, Software Engineering, Computer Science, Theory & Methods, Computer Science, mutation testing, commit-relevant mutants, continuous integration, regression testing, machine learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10138491 |
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