Inforence: Effective Fault Localization Based on Information-Theoretic Analysis and Statistical Causal Inference

TL;DR

Inforence is a novel fault localization method that uses information-theoretic analysis and causal inference to identify and rank groups of interdependent statements likely causing program failures, outperforming existing techniques.

Contribution

The paper introduces Inforence, a new fault localization approach that considers statement interdependence and causal effects, improving accuracy over state-of-the-art methods.

Findings

Outperforms existing fault localization techniques on multiple benchmarks.

Effectively identifies groups of interdependent statements causing failures.

Proves effective on both single-fault and multi-fault programs.

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.