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Fault localization techniques aim to localize faulty statements using the information gathered from both passed and failed test cases. We present a mutation-based fault localization technique called MuSim. MuSim identifies the faulty statement based on its computed proximity to different mutants. We study the performance of MuSim by using four different similarity metrics. To satisfactorily measure the effectiveness of our proposed approach, we present a new evaluation metric called Mut_Score. Based on this metric, on an average, MuSim is 33.21% more effective than existing fault localization techniques such as DStar, Tarantula, Crosstab, Ochiai. [ABSTRACT FROM AUTHOR]

In this article, we propose a hierarchical fault localization technique using a deep neural network (DNN). First, we prioritize the functions of a program based on their suspiciousness score. Subsequently, the fault is localized to specific statements within the top k suspected functions, where the value of k is determined heuristically. We use two function-level features to train a DNN for fault localization at the function level. Subsequently, the invocation information of the statements of the top-k functions is used to train another neural network to localize the faulty statement. We also report an extension to our approach for localizing multiple faults. This involves partitioning the failed test cases into clusters such that they target different faults. Our empirical evaluation indicates that our proposed approach requires examining 30.05 to 50.74% less code on an average, as compared to related fault localization techniques. [ABSTRACT FROM AUTHOR]