Rights statement: © ACM, 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ISSTA 2016 Proceedings of the 25th International Symposium on Software Testing and Analysis http://dx.doi.org/10.1145/2931037.2931039
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| Publication date | 18/07/2016 |
|---|---|
| Host publication | ISSTA 2016 Proceedings of the 25th International Symposium on Software Testing and Analysis |
| Place of Publication | New York |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 330-341 |
| Number of pages | 12 |
| ISBN (electronic) | 9781450343909 |
| <mark>Original language</mark> | English |
| Event | 25th International Symposium on Software Testing and Analysis, ISSTA 2016 - Saarbrucken, Germany Duration: 18/07/2016 → 20/07/2016 |
| Conference | 25th International Symposium on Software Testing and Analysis, ISSTA 2016 |
|---|---|
| Country/Territory | Germany |
| City | Saarbrucken |
| Period | 18/07/16 → 20/07/16 |
| Conference | 25th International Symposium on Software Testing and Analysis, ISSTA 2016 |
|---|---|
| Country/Territory | Germany |
| City | Saarbrucken |
| Period | 18/07/16 → 20/07/16 |
We introduce mutation-aware fault prediction, which leverages additional guidance from metrics constructed in terms of mutants and the test cases that cover and detect them. We report the results of 12 sets of experiments, applying 4 Different predictive modelling techniques to 3 large real-world systems (both open and closed source). The results show that our proposal can significantly (p ≤ 0:05) improve fault prediction performance. Moreover, mutation-based metrics lie in the top 5% most frequently relied upon fault predictors in 10 of the 12 sets of experiments, and provide the majority of the top ten fault predictors in 9 of the 12 sets of experiments.