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Accurate software defect prediction could help software practitionersallocate test resources to defect-prone modules effectively and efficiently. Inthe last decades, much effort has been devoted to build accurate defectprediction models, including developing quality defect predictors and modelingtechniques. However, current widely used defect predictors such as code metricsand process metrics could not well describe how software modules change overthe project evolution, which we believe is important for defect prediction. Inorder to deal with this problem, in this paper, we propose to use theHistorical Version Sequence of Metrics (HVSM) in continuous software versionsas defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN),a popular modeling technique, to take HVSM as the input to build softwareprediction models. The experimental results show that, in most cases, theproposed HVSM-based RNN model has a significantly better effort-aware rankingeffectiveness than the commonly used baseline models.

Connecting Software Metrics across Versions to Predict Defects