PCD-DAug uses a diffusion model guided by program-slicing and PCA-derived contexts to generate synthetic failing test cases, improving fault localization effectiveness across six methods.
Deepfl: Integrating multiple fault diagnosis dimensions for deep fault localization
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Principal Context-aware Diffusion Guided Data Augmentation for Fault Localization
PCD-DAug uses a diffusion model guided by program-slicing and PCA-derived contexts to generate synthetic failing test cases, improving fault localization effectiveness across six methods.