SAL: An effective method for software defect prediction

摘要

For software quality assurance, software defect prediction (SDP) has drawn a great deal of attention in recent years. Its goal is to reduce verification cost, time and effort by predicting the defective modules efficiently. In SDP, proper attribute selection plays a significant role. However, selection of proper attributes and their representation in an efficient way are very challenging due to the lacking of standard set of attributes. To address these issues, we introduce Selection of Attribute with Log filtering (SAL) to select a proper set of attributes. Our proposed attribute selection process can effectively select the best set of attributes, which are relevant for the discrimination of defected and non-defected software modules. Further, we adopt log filtering to pre-process the input data. We have evaluated the proposed attribute selection method using several widely used publicly available datasets. The simulation results demonstrate that our method is more effective to improve the accuracy of SDP than the existing state-of-the-art methods.