Mitigating the Effect of Class Imbalance in Fault Localization Using Context-aware Generative Adversarial Network

TL;DR

This paper introduces CGAN4FL, a novel data augmentation method using context-aware GANs to address class imbalance in fault localization, significantly enhancing its accuracy by synthesizing failing test cases based on program dependencies.

Contribution

The paper presents a new approach that leverages program dependencies and GANs to generate failing test cases, improving fault localization performance under class imbalance.

Findings

CGAN4FL significantly improves fault localization effectiveness.

Promotes MLP-FL by over 200% in Top-1 accuracy.

Achieves notable gains in Top-5 and Top-10 metrics.

Abstract

Fault localization (FL) analyzes the execution information of a test suite to pinpoint the root cause of a failure. The class imbalance of a test suite, i.e., the imbalanced class proportion between passing test cases (i.e., majority class) and failing ones (i.e., minority class), adversely affects FL effectiveness. To mitigate the effect of class imbalance in FL, we propose CGAN4FL: a data augmentation approach using Context-aware Generative Adversarial Network for Fault Localization. Specifically, CGAN4FL uses program dependencies to construct a failure-inducing context showing how a failure is caused. Then, CGAN4FL leverages a generative adversarial network to analyze the failure-inducing context and synthesize the minority class of test cases (i.e., failing test cases). Finally, CGAN4FL augments the synthesized data into original test cases to acquire a class-balanced dataset for FL. Our experiments show that CGAN4FL significantly improves FL effectiveness, e.g., promoting MLP-FL by 200.00%, 25.49%, and 17.81% under the Top-1, Top-5, and Top-10 respectively.