arXiv AI By Dangfeng Pan, Zhensu Sun, Cenyuan Zhang, David Lo, Xiaoning Du

Talking to Itself While Coding: What Makes Comments Help Code Generation?

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The study investigates how natural-language comments influence code generation by large language models. Observational and controlled experiments on LiveCodeBench reveal that comment frequency and general intent do not predict success, but comments derived from correct solutions significantly improve recipient model performance by an average of 17.2%. Conversely, comments from failed solutions or unrelated problems either offer no benefit or even reduce performance, and most models cannot fully recover the advantage of well‑crafted comments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Jun 12

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.

By Kla Tantithamthavorn, Hong Yi Lin, Patanamon Thongtanunam, Wachiraphan Charoenwet, Minwoo Jeong, Ming Wu