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Inforence: effective fault localization based on information-theoretic analysis and statistical causal inference

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Abstract

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.

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Authors and Affiliations

  1. Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Farid Feyzi & Saeed Parsa

Authors
  1. Farid Feyzi
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  2. Saeed Parsa
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Correspondence to Saeed Parsa.

Additional information

Farid Feyzi received the MS degree in Software Engineering from the Sharif University of Technology, Iran in 2012. He is currently a PhD candidate in the Department of Computer Engineering at Iran University of Science and Technology. His research focus is on developing statistical algorithms to improve software quality with an emphasis on statistical fault localization and automated test data generation.

Saeed Parsa received his BS in Mathematics and Computer Science from Sharif University of Technology, Iran, his MS degree in Computer Science from the University of Salford in England, and his PhD in Computer Science from the University of Salford, England. He is an associate professor of Computer Science at the Iran University of Science and Technology. His research interests include software engineering, software testing and debugging, and algorithms.

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Feyzi, F., Parsa, S. Inforence: effective fault localization based on information-theoretic analysis and statistical causal inference. Front. Comput. Sci. 13, 735–759 (2019). https://doi.org/10.1007/s11704-017-6512-z

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