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Fault localization is one of the most important and difficult tasks in the software debugging process. Therefore, several methods have been proposed to automate and improve this process. Mutation-based fault localization is one of the states of the art techniques that try to locate faults by executing different mutants of the faulty program. In addition to favorable results, it is along with a massive increase in mutation execution cost. In this paper, we propose a new mutation-based fault localization approach called SMBFL, that aim to reduce the execution cost by reducing the number of statements to be mutated. As fewer mutants execute with SMBFL, the whole process will become faster and the cost will decrease. SMBFL only examines the statements in the dynamic slice of the program under test. The statements that present in the dynamic slice have a direct effect on the execution of the program with the specified test case. In the SMBFL method, the suspiciousness score of program statements is measured based on the entropy of their mutants. The proposed formula, MuEn, determines the suspiciousness score based on the result of executing mutants of each statement of the program. SMBFL is evaluated during a series of tests. The results show a relative increase in the accuracy of fault localization, by an average of 14.2%, and a decrease in the execution time of the fault localization process, by an average of 24.3%. Finally, the MuEn formula applies the least execution overhead to the fault localization process. [ABSTRACT FROM AUTHOR]

Despite the proven applicability of the spectrum-based fault localization (SBFL) methods, their effectiveness may be degraded due to the presence of coincidental correctness, which occurs when faults fail to propagate, i.e., their execution does not result in failures. This article aims at improving SBFL effectiveness by mitigating the effect of coincidentally correct test cases. In this regard, given a test suite in which each test has been classified as failing or passing and each faulty program has a single-bug, we present a program slicing-based technique to identify a set of program entities that directly affect the program output when executed with failing test cases, called failure candidate causes (FCC). We then use FCC set to identify test cases that can be marked as being coincidentally correct. These tests are identified based on two heuristics: the average suspiciousness score of the statements that directly affect the program output and the coverage ratio of those statements. To evaluate our approach, we used several evaluation metrics and conducted extensive experiments on programs containing single and multiple bugs, including both real and seeded faults. The empirical results demonstrate that the proposed heuristics can alleviate the coincidental correctness problem and improve the accuracy of SBFL techniques. [ABSTRACT FROM AUTHOR]

The aim of this paper is to build an online failure prediction classifier for monitoring the behavior of programs. The classifier predicts the termination state of the program execution paths as failing or passing. This could be achieved by mapping each execution path as a vector into a feature space whose dimensions represent common sub-paths amongst failing and passing execution paths. The main contribution of this paper is to treat the failure prediction problem as a classification task of execution paths in a customized feature space. The main dilemma is the size and the number of space dimensions, affecting the speed of the classifier. The size of the dimensions could be reduced by shortening the length of the common sub-paths, used as the space dimensions. The length of common sub-paths is affected by repeated patterns in program executions. Replacing the consecutively repeated patterns with only a single iteration in execution paths, reduces the size of the common sub-paths. The number of dimensions could be reduced by removing dimensions which have projection onto others. This paper proposes two kernels which measure similarity amongst execution paths in an implicit feature space with reduced dimensionality. Our experiments demonstrate a significant reduction in time overhead of the failure prediction classifier while preserving accuracy. [ABSTRACT FROM AUTHOR]

In this letter, a new online anomaly detection approach for software systems is proposed. The novelty of the proposed approach is to apply a new semantic kernel function for a support vector machine (SVM) classifier to detect faultsuspicious execution paths at runtime in a reasonable amount of time. The kernel uses a new sequence matching algorithm to measure similarities among program execution paths in a customized feature space whose dimensions represent the largest common subpaths among the execution paths. To increase the precision of the SVM classifier, each common subpath is given weights according to its ability to discern executions as correct or anomalous. Experiment results show that compared with the known kernels, the proposed SVM kernel will improve the time overhead of online anomaly detection by up to 170%, while improving the precision of anomaly alerts by up to 140% . [ABSTRACT FROM AUTHOR]

Loop parallelization is an important issue in the acceleration of the execution of scientific programs. To exploit parallelism in loops a system of equations representing the dependencies between the loop iterations and a system of non-equations indicating the loop boundary conditions has to be solved. This is a NP-Complete problem. Our major contribution in this paper has been to apply genetic algorithm to solve system of equation and non-equation resulted from loop dependency analysis techniques to find two dependent loop iterations. We use distance vector to find the rest of dependencies. [ABSTRACT FROM AUTHOR]