Are LLMs Reliable Code Reviewers? Systematic Overcorrection in Requirement Conformance Judgement

TL;DR

This paper reveals that large language models often misjudge code compliance with natural language requirements, especially with detailed prompts, exposing reliability issues in LLM-based code review systems.

Contribution

The study systematically uncovers LLM failures in code requirement conformance judgment and proposes a fix-guided verification method to improve reliability.

Findings

LLMs frequently misclassify correct code as non-compliant.

More detailed prompts increase misjudgment rates.

The proposed verification filter improves review accuracy.

Abstract

Large language models (LLMs) have become essential tools in software development, widely used for requirements engineering, code generation and review tasks. Software engineers often rely on LLMs to verify if code implementation satisfy task requirements, thereby ensuring code robustness and accuracy. However, it remains unclear whether LLMs can reliably determine code against the given task descriptions, which is usually in a form of natural language specifications. In this paper, we uncover a systematic failure of LLMs in matching code to natural language requirements. Specifically, with widely adopted benchmarks and unified prompts design, we demonstrate that LLMs frequently misclassify correct code implementation as non-compliant or defective. Surprisingly, we find that more detailed prompt design, particularly with those requiring explanations and proposed corrections, leads to higher misjudgment rates, highlighting critical reliability issues for LLM-based code assistants. We further analyze the mechanisms driving these failures and evaluate the reliability of rationale-required judgments. Building on these findings, we propose a Fix-guided Verification Filter that treats the model proposed fix as executable counterfactual evidence, and validates the original and revised implementations using benchmark tests and spec-constrained augmented tests. Our results expose previously under-explored limitations in LLM-based code review capabilities, and provide practical guidance for integrating LLM-based reviewers with safeguards in automated review and development pipelines.