HalluJudge: A Reference-Free Hallucination Detection for Context Misalignment in Code Review Automation
arXiv:2601.19072v3 Announce Type: replace-cross Abstract: Large Language models (LLMs) have shown strong capabilities in code review automation, such as review comment generation, yet they suffer from hallucinations -- where the generated review comments are ungrounded in the actual code -- poses a significant challenge to the adoption of LLMs in code review workflows. To address this, we explore effective and scalable methods for a hallucination detection in LLM-generated code review comments without the reference. In this work, we design HalluJudge that aims to assess the grounding of genera
The rapid deployment and increasing sophistication of LLMs in software development necessitate robust methods for ensuring their reliability, especially in critical tasks like code review.
Addressing hallucinations in LLM-generated code reviews is crucial for wider adoption and trust in AI-driven software development tools, impacting efficiency and reducing human oversight needs.
The ability to accurately detect hallucinations without a reference significantly de-risks the integration of LLMs into automated code review workflows, accelerating their practical application.
- · Software development companies
- · AI/ML tool vendors
- · Open-source communities
- · Developers leveraging AI for code review
- · Manual code review services
- · Companies with low-quality AI integration
- · Bug bounty platforms (potentially reduced volume)
Increased reliability and adoption of LLMs in software development, particularly for code maintenance and quality.
Reduced incidence of subtle, AI-introduced bugs or vulnerabilities due to improved review automation.
Accelerated pace of software innovation as development cycles become leaner and more automated.
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Read at arXiv cs.AI