Reasoning Distillation for Lightweight Automated Program Repair

TL;DR

This work addresses how to improve fix-type classification for compact automated program repair models in resource-constrained settings by using lightweight symbolic reasoning distillation from a large teacher. A CodeT5-based student is trained under label-only and reasoning-distilled conditions, evaluated on IntroClass, and shown to achieve higher macro-averaged performance, particularly for less frequent bug types, while reliably reproducing teacher-like reasoning traces. The study finds a strong correlation between correct reasoning and accurate fix-type predictions, though reasoning alone does not fully determine the correct label, and discusses a JSON-based distillation alternative that is more expressive but data-hungry. Overall, symbolic reasoning distillation enhances interpretability and robustness of small models without adding architectural complexity, suggesting practical benefits for deployable, transparent program repair tools in constrained environments.

Abstract

We study whether lightweight symbolic reasoning supervision can improve fix type classification in compact automated program repair models. Small code models are attractive for resource-constrained settings, but they typically produce only a single prediction, making it unclear whether they learn meaningful program structure or rely on shallow correlations. We propose a reasoning distillation approach in which a large teacher model provides structured symbolic reasoning tags alongside fix-type labels. These tags capture high-level causal properties of bugs without relying on free-form explanations. We train a CodeT5-based student model under label-only and reasoning-distilled settings on the IntroClass benchmark. Reasoning supervision consistently improves macro averaged performance, particularly on less frequent bug categories, without increasing model size or complexity. We further analyze the relationship between reasoning accuracy and fix-type prediction, showing that correct reasoning traces strongly correlate with correct predictions, while not fully determining them. Our results suggest that symbolic reasoning distillation is a practical way to improve interpretability and robustness in lightweight program repair models.