A Scenario-Based Approach to Predicting Software Defects Using Compressed C4.5 Model

Defect prediction approaches use software metrics and fault data to learn which software properties are associated with what kinds of software faults in programs. One trend of existing techniques is to predict the software defects in a program construct (file, class, method, and so on) rather than in a specific function scenario, while the latter is important for assessing software quality and tracking the defects in software functionalities. However, it still remains a challenge in that how a functional scenario is derived and how a defect prediction technique should be applied to a scenario. In this paper, we propose a scenario-based approach to defect prediction using compressed C4.5 model. The essential idea of this approach is to use a k-medoids algorithm to cluster functions followed by deriving functional scenarios, and then to use the C4.5 model to predict the fault in the scenarios. We have also conducted an experiment to evaluate the scenario-based approach and compared it with a file-based prediction approach. The experimental results show that the scenario-based approach provides with high performance by reducing the size of the decision tree by 52.65% on average and also slightly increasing the accuracy.