In this paper, a novel approach, Inforence, is proposed to isolate the suspicious codes that likely contain faults. Inforence employs a feature selection method, based on mutual information, to identify those bug-related statements that may cause the program to fail. Because the majority of a program faults may be revealed as undesired joint effect of the program statements on each other and on program termination state, unlike the state-of-the-art methods, Inforence tries to identify and select groups of interdependent statements which altogether may affect the program failure. The interdependence amongst the statements is measured according to their mutual effect on each other and on the program termination state. To provide the context of failure, the selected bug-related statements are chained to each other, considering the program static structure. Eventually, the resultant cause-effect chains are ranked according to their combined causal effect on program failure. To validate Inforence, the results of our experiments with seven sets of programs include Siemens suite, gzip, grep, sed, space, make and bash are presented. The experimental results are then compared with those provided by different fault localization techniques for the both single-fault and multi-fault programs. The experimental results prove the outperformance of the proposed method compared to the state-of-the-art techniques.research focus is on developing statistical algorithms to improve software quality with an emphasis on statistical fault localization and automated test data generation.

in this paper, we have developed an approach to generate test data for path coverage based testing. The main challenge of this kind testing lies in its ability to build efficiently such a test suite in order to minimize the number of rejects. We address this problem with a novel divide-andconquer approach based on adaptive random testing strategy. Our approach takes as input the constraints of an executable path and computes a tight over-approximation of their associated sub-domain by using a dynamic domain partitioning approach. We implemented this approach and got experimental results that show the practical benefits compared to existing approaches. Our method generates less invalid inputs and is capable of obtaining the sub-domain of many complex constraints. Abstract-software testing; test data; path coverage; random testing; adaptive random testing.I.

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Despite the proven applicability of the statistical methods in automatic fault localization, these approaches are biased by data collected from different executions of the program. This biasness could result in unstable statistical models which may vary dependent on test data provided for trial executions of the program. To resolve the difficulty, in this article a new 'fault-proneness'-aware statistical approach based on Elastic-Net regression, namely FPA-FL is proposed. The main idea behind FPA-FL is to consider the static structure and the fault-proneness of the program statements in addition to their dynamic correlations with the program termination state. The grouping effect of FPA-FL is helpful for finding multiple faults and supporting scalability. To provide the context of failure, causeeffect chains of program faults are discovered. FPA-FL is evaluated from different viewpoints on well-known test suites. The results reveal high fault localization performance of our approach, compared with similar techniques in the literature.