Sousuke Amasaki
ABSTRACT
Context: Cross-project defect prediction (CPDP) research has been popular, and many CPDP methods have been proposed so far. As the straightforward use of Cross-project (CP) data was useless, those methods filter, weigh, and adapt CP data for a target project data. This idea would also be useful for a project having past defect data. Objective: To evaluate the applicability of CPDP methods for multi-versions projects. The evaluation focused on the relationship between the performance change and the proximity of older release data to a target project. Method: We conducted experiments that compared the predictive performance between using older version data with and without Nearest Neighbor (NN) filter, a classic CPDP method. Results: NN-filter could not make clear differences in predictive performance. Conclusions: NN-filter was not helpful for improving predictive performance with older release data.
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Index Terms
- On Applicability of Cross-project Defect Prediction Method for Multi-Versions Projects
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