A clustering algorithm for software fault prediction

Software metrics are used for predicting whether modules of software project are faulty or fault free. Timely prediction of faults especially accuracy or computation faults improve software quality and hence its reliability. As we can apply various distance measures on traditional K-means clustering algorithm to predict faulty or fault free modules. Here, in this paper we have proposed K-Sorensen-means clustering that uses Sorensen distance for calculating cluster distance to predict faults in software projects. Proposed algorithm is then trained and tested using three datasets namely, JM1, PCI and CM1 collected from NASA MDP. From these three datasets requirement metrics, static code metrics and alliance metrics (combining both requirement metrics and static code metrics) have been built and then K-Sorensen-means applied on all datasets to predict results. Alliance metric model is found to be the best prediction model among three models. Results of K-Sorensen-means clustering shown and corresponding ROC curve has been drawn. Results of K-Sorensen-means are then compared with K-Canberra-means clustering that uses other distance measure for evaluating cluster distance.