FPA-FL: Incorporating Static Fault-proneness Analysis into Statistical Fault Localization

TL;DR

FPA-FL is a novel fault localization method that integrates static fault-proneness analysis with Elastic-Net regression to improve stability, scalability, and fault detection accuracy in software testing.

Contribution

It introduces a fault-proneness-aware statistical approach combining static analysis with dynamic data, enhancing fault localization effectiveness over existing methods.

Findings

High fault localization accuracy demonstrated on test suites.

Effective identification of multiple faults and scalability support.

Outperforms similar techniques in the literature.

Abstract

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, cause-effect 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.