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 data sets, 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...

• Type: Journal
• Year: 2008
• Where: TSE
• Authors: Stefan Lessmann, Bart Baesens, Christophe Mues, Swantje Pietsch
• Keywords: Classification Models, Metric-based Classification, Software Defect Prediction, TSE 2008