Data Mining Techniques for Software Defect Prediction

Software defect prediction work focuses on the number of defects remaining in a software system. A prediction of the number of remaining defects in an inspected artefact can be used for decision making. An accurate prediction of the number of defects in a software product during system testing contributes not only to the management of the system testing process but also to the estimation of the product’s required maintenance. Defective software modules cause software failures, increase development and maintenance costs, and decrease customer satisfaction. It strives to improve software quality and testing efficiency by constructing predictive models from code attributes to enable a timely identification of fault-prone modules. In this paper, we will discuss data mining techniques that are association mining, classification and clustering for software defect prediction. This helps the developers to detect software defects and correct them. Unsupervised techniques may be used for defect prediction in software modules, more so in those cases where defect labels are not available.