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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Re-evaluating method-level bug prediction |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| ISBN | 978-1-5386-4969-5 |
| Page Range | 592 - 601 |
| Event Title | 25th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) |
| Event Type | conference |
| Event Location | Campobasso |
| Event Start Date | April 20 - 2018 |
| Event End Date | April 23 - 2018 |
| Series Name | REproducibility Studies and NEgative Results |
| Publisher | IEEE |
| Abstract Text | Bug prediction is aimed at supporting developers in the identification of code artifacts more likely to be defective. Researchers have proposed prediction models to identify bug prone methods and provided promising evidence that it is possible to operate at this level of granularity. Particularly, models based on a mixture of product and process metrics, used as independent variables, led to the best results. In this study, we first replicate previous research on method- level bug prediction on different systems/timespans. Afterwards, we reflect on the evaluation strategy and propose a more realistic one. Key results of our study show that the performance of the method-level bug prediction model is similar to what previously reported also for different systems/timespans, when evaluated with the same strategy. However—when evaluated with a more realistic strategy—all the models show a dramatic drop in performance exhibiting results close to that of a random classifier. Our replication and negative results indicate that method-level bug prediction is still an open challenge. |
| Digital Object Identifier | 10.1109/SANER.2018.8330264 |
| Other Identification Number | merlin-id:16637 |
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| Funders | Swiss National Science Foundation: SNF Project No. PP00P2_170529 ; European Commission: SENECA - EU MSCA-ITN-2014-EID no.642954 |