Poster: Identification of Methods with Low Fault Risk
This helps testers prioritize untested code areas to increase fault detection probability, but it is incremental as it builds on existing defect prediction methods.
The paper tackles the problem of limited testing resources by introducing an inverse defect prediction approach to identify methods with low fault risk, showing that on average 31.6% of methods have low fault risk and contain only 5.8% of all faults.
Test resources are usually limited and therefore it is often not possible to completely test an application before a release. Therefore, testers need to focus their activities on the relevant code regions. In this paper, we introduce an inverse defect prediction approach to identify methods that contain hardly any faults. We applied our approach to six Java open-source projects and show that on average 31.6% of the methods of a project have a low fault risk; they contain in total, on average, only 5.8% of all faults. Furthermore, the results suggest that, unlike defect prediction, our approach can also be applied in cross-project prediction scenarios. Therefore, inverse defect prediction can help prioritize untested code areas and guide testers to increase the fault detection probability.