arXiv:2606.03852v1 Announce Type: cross 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
The rapid advancement and widespread adoption of Large Language Models (LLMs) in software development creates an urgent need for efficient debugging and refinement tools.
This development significantly enhances the practical utility and reliability of LLM-generated code by addressing a core limitation: bug localization and correction.
The ability to provide fine-grained diagnostic feedback directly informs LLMs on where to fix bugs, moving away from coarse-grained or high-level indicators and accelerating code development cycles.
- · AI developers
- · Software engineering teams
- · Companies adopting LLMs for coding
- · Cloud providers
- · Manual debugging services
- · Traditional code review processes
LLMs can generate more robust and functional code with fewer iterations.
This leads to increased productivity for software developers and potentially accelerates innovation in various tech sectors.
The enhanced reliability of AI-generated code could reduce the overall cost and time-to-market for new software products, shifting competitive landscapes.
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Read at arXiv cs.AI