ReDef creates a revert-anchored dataset of 3,164 defective and 10,268 clean code modifications and shows that code language models perform better with diff encodings but maintain stable performance under counterfactual perturbations, indicating reliance on superficial cues.
Just-in-time software defect prediction via bi-modal change representation learning.Journal of Systems and Software, 219:112253
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ReDef: Do Code Language Models Truly Understand Code Changes for Just-in-Time Software Defect Prediction?
ReDef creates a revert-anchored dataset of 3,164 defective and 10,268 clean code modifications and shows that code language models perform better with diff encodings but maintain stable performance under counterfactual perturbations, indicating reliance on superficial cues.