  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.
