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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | A search-based training algorithm for cost-aware defect prediction |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published electronically before print/final form (Epub ahead of print) |
| Language |
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| Page Range | Epub ahead of print |
| Event Title | Genetic and Evolutionary Computation Conference |
| Event Type | conference |
| Event Location | Denver |
| Event Start Date | July 20 - 2016 |
| Event End Date | July 24 - 2016 |
| Place of Publication | Denver |
| Abstract Text | Research has yielded approaches to predict future defects in software artifacts based on historical information, thus assisting companies in effectively allocating limited development resources and developers in reviewing each others’ code changes. Developers are unlikely to devote the same effort to inspect each software artifact predicted to contain defects, since the effort varies with the artifacts’ size (cost) and the number of defects it exhibits (effectiveness). We propose to use Genetic Algorithms (GAs) for training prediction models to maximize their cost-effectiveness. We evaluate the approach on two well-known models, Regression Tree and Generalized Linear Model, and predict defects between multiple releases of six open source projects. Our results show that regression models trained by GAs significantly outperform their traditional counterparts, improving the cost-effectiveness by up to 240%. Often the top 10% of predicted lines of code contain up to twice as many defects. |
| Digital Object Identifier | 10.1145/2908812.2908938 |
| Other Identification Number | merlin-id:13244 |
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