A Bayes Network Classification Approach for Finding Faulty Modules in Open Source Software Systems
Prediction of fault-prone modules provides one way to support software quality engineering through improved scheduling and project control. There are many metrics and techniques available to investigate the accuracy of fault prone classes which may help software organizations for planning and performing testing activities. Bayes algorithms are being successfully applied for solving both classification and regression problems. It is therefore important to investigate the capabilities of Bayes Network Classification algorithm in predicting software quality. In order to perform the analysis the authors validate the performance of the Bayes Network based Algorithm using open source software JEdit.