Fine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revision

Researchers propose fine-grained confidence calibration methods for large language models in automated code revision tasks, addressing the limitation of traditional global calibration approaches. By applying local Platt-scaling to task-specific confidence scores, the study demonstrates improved calibration accuracy across multiple code repair and refinement tasks, enabling developers to better trust LLM outputs.

This research addresses a critical gap in making LLMs more reliable for software engineering applications. While LLMs have demonstrated impressive coding capabilities, their tendency to produce incorrect outputs without reliable confidence signals limits their practical utility in production environments. The study reveals that existing calibration methods, effective in other generative tasks, fail to adequately capture the granular decision-making required in code revision work where localized edits determine correctness.

The motivation stems from practical development workflows where engineers must decide whether to accept, modify, or reject AI-generated code fixes. Current post-hoc calibration techniques apply uniform scaling across entire model outputs, missing the nuanced confidence variations within specific code edits. Fine-grained approaches that differentiate confidence at the token or edit level provide more actionable signals to developers, reflecting where models genuinely understand code semantics versus where they guess.

The research's significance extends across the software development industry, where AI-assisted coding tools are rapidly proliferating. Better-calibrated confidence scores reduce false positives that waste developer time and false negatives that cause missed optimization opportunities. The testing across 14 models of varying sizes suggests the findings generalize broadly, making this applicable to both large proprietary systems and open-source alternatives that organizations increasingly deploy.

The work establishes calibration quality as a competitive differentiator in AI coding tools. Organizations integrating these methods can offer developers clearer guidance on model reliability, potentially accelerating AI adoption in enterprise environments where trust and explainability remain barriers to deployment.

• →Fine-grained confidence calibration outperforms traditional global approaches for automated code revision tasks
• →Local Platt-scaling applied to task-specific confidence scores reduces miscalibration across probability intervals
• →Results validated across 14 models of different sizes, suggesting broad applicability to LLM-based coding tools
• →Better calibration enables developers to make faster acceptance decisions and align expectations with model capabilities
• →The approach addresses sample-dependent miscalibration where correctness depends on localized edits rather than global outputs