On Fault Localization Using Machine Learning Techniques Early prediction of faulty modules provides a way to support software quality assurance activities through improved scheduling and effectiveness of process control. Different fault prediction models have been proposed by researchers for early prediction of faults. However, despite predicting faulty classes, these techniques donu0027t provide information about identifying the location of occurrence of faults. In this paper, we aim to propose a methodology not only to predict faults but also for fault localization as well. We first make use of various datasets to build a software fault prediction model based. We make use of random forest machine learning technique to train our model. We also extract CKmetrics from different modules and we then make use of these metrics for fault localization. In order to do validation, we make use of case studies where we do fault prediction, fault localization and then run test cases to see if our prediction was correct.

Combining SpectrumBased Fault Localization and Statistical Debugging: An Empirical Study Program debugging is a timeconsuming task, and researchers have proposed different kinds of automatic fault localization techniques to mitigate the burden of manual debugging. Among these techniques, two popular families are spectrumbased fault localization (SBFL) and statistical debugging (SD), both localizing faults by collecting statistical information at runtime. Though the ideas are similar, the two families have been developed independently and their combinations have not been systematically explored. In this paper we perform a systematical empirical study on the combination of SBFL and SD. We first build a unified model of the two techniques, and systematically explore four types of variations, different predicates, different risk evaluation formulas, different granularities of data collection, and different methods of combining suspicious scores. Our study leads to several findings. First, most of the effectiveness of the combined approach contributed by a simple type of predicates: branch conditions. Second, the risk evaluation formulas of SBFL significantly outperform that of SD. Third, finegrained data collection significantly outperforms coarsegrained data collection with a little extra execution overhead. Fourth, a linear combination of SBFL and SD predicates outperforms both individual approaches. According to our empirical study, we propose a new fault localization approach, PREDFL (Predicatebased Fault Localization), with the best configuration for each dimension under the unified model. Then, we explore its complementarity to existing techniques by integrating PREDFL with a stateoftheart fault localization framework. The experimental results show that PREDFL can further improve the effectiveness of stateoftheart fault localization techniques. More concretely, integrating PREDFL results in an up to 20.8% improvement w.r.t the faults successfully located at Top1, which reveals that PREDFL complements existing techniques.