FLARE: Fine-Grained Diagnostic Feedback for LLM Code Refinement

Abstract:Large language models often generate code with bugs. Existing methods rely on feedback signals such as test failures and self-critiques to iteratively refine the generated code. Such signals are either too coarse-grained or too high-level, which is not sufficient to inform the model where to fix the bug. In this work, we present Flare, an iterative framework with a lightweight diagnostic model that predicts line-level suspiciousness signals for bug localization and code refinement. Given the inherent uncertainty of diagnostic predictions, Flare searches over the top-k suspicious regions and selects the best candidate according to execution outcomes. Experiments on LiveCodeBench and BigCodeBench with five base LLMs show that, even without candidate search (k=1), Flare outperforms the strongest baseline with an absolute improvement from 1.72% to 7.42%. Furthermore, searching over 10 candidates yields an average improvement of 8.50% compared with no candidate search. When evaluated in isolation, our lightweight diagnostic model achieves the best performance compared with recent fault localization methods, demonstrating that it can provide reliable fine-grained guidance for code refinement.

Software Engineering (cs.SE); Artificial Intelligence (cs.AI)

arXiv:2606.03852 [cs.SE]

(or arXiv:2606.03852v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2606.03852 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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