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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Comparing fine-grained source code changes and code churn for 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 | 978-1-4503-0574-7 |
| Page Range | 83 - 92 |
| Event Title | 8th working conference on Mining software repositories |
| Event Type | conference |
| Event Location | Honolulu, HI USA |
| Event Start Date | May 21 - 2011 |
| Event End Date | May 22 - 2011 |
| Series Name | Proceedings of the 8th Working Conference on Mining Software Repositories |
| Place of Publication | New York, NY, USA |
| Publisher | Association for Computing Machinery |
| Abstract Text | A significant amount of research effort has been dedicated to learning prediction models that allow project managers to efficiently allocate resources to those parts of a software system that most likely are bug-prone and therefore critical. Prominent measures for building bug prediction models are product measures, e.g., complexity or process measures, such as code churn. Code churn in terms of lines modified (LM) and past changes turned out to be significant indicators of bugs. However, these measures are rather imprecise and do not reflect all the detailed changes of particular source code entities during maintenance activities. In this paper, we explore the advantage of using fine-grained source code changes (SCC) for bug prediction. SCC captures the exact code changes and their semantics down to statement level. We present a series of experiments using different machine learning algorithms with a dataset from the Eclipse platform to empirically evaluate the performance of SCC and LM. The results show that SCC outperforms LM for learning bug prediction models. |
| Related URLs | |
| Digital Object Identifier | 10.1145/1985441.1985456 |
| Other Identification Number | merlin-id:3851 |
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