Benchmarking classification models for software defect prediction: a proposed framework and novel findings

Software defect prediction strives to improve software quality and testing efficiency by constructing predictive classification models from code attributes to enable a timely identification of fault-prone modules. Several classification models have been evaluated for this task. However, due to inconsistent findings regarding the superiority of one classifier over another and the usefulness of metric-based classification in general, more research is needed to improve convergence across studies and further advance confidence in experimental results. We consider three potential sources for bias: comparing classifiers over one or a small number of proprietary datasets, relying on accuracy indicators that are conceptually inappropriate for software defect prediction and cross-study comparisons, and finally, limited use of statistical testing procedures to secure empirical findings. To remedy these problems, a framework for comparative software defect prediction experiments is proposed and applied in a large-scale empirical comparison of 22 classifiers over ten public domain datasets from the NASA Metrics Data repository. Our results indicate that the importance of the particular classification algorithm may have been overestimated in previous research since no significant performance differences could be detected among the top-17 classifiers