SANER 2018

2018 IEEE 25th International Conference on Software Analysis, Evolution, and Reengineering (SANER), March 20-23, 2018, Campobasso, Italy

Desktop Layout

Defect Prediction
Technical Research Papers
Room 2
Connecting Software Metrics across Versions to Predict Defects
Yibin Liu, Yanhui Li, Jianbo Guo, Yuming Zhou, and Baowen Xu
(Nanjing University, China; Tsinghua University, China)
Abstract: Accurate software defect prediction could help software practitioners allocate test resources to defect-prone modules effectively and efficiently. In the last decades, much effort has been devoted to build accurate defect prediction models, including developing quality defect predictors and modeling techniques. However, current widely used defect predictors such as code metrics and process metrics could not well describe how software modules change over the project evolution, which we believe is important for defect prediction. In order to deal with this problem, in this paper, we propose to use the Historical Version Sequence of Metrics (HVSM) in continuous software versions as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN), a popular modeling technique, to take HVSM as the input to build software prediction models. The experimental results show that, in most cases, the proposed HVSM-based RNN model has significantly better effort-aware ranking effectiveness than the commonly used baseline models.


Time stamp: 2019-09-16T09:14:47+02:00