Effective Fault Localization using an Ensemble Classifier

Fault localization is possibly the most time consuming and tedious task in the process of program debugging. To alleviate this issue, we propose an ensemble of fault localization techniques. In our proposed ensemble technique, we have used DStar and Tarantula from the spectrum based fault localization family. Along with these two methods, BPNN and RBFNN are used from neural network based fault localization techniques. We also propose a novel CNN based fault localization method to strengthen the proposed ensemble classifier. We have proposed a new metric to measure the effectiveness of fault localization techniques more accurately. On an average, our proposed ensemble method is 16.76% to 38.47% more effective than the existing fault localization techniques.