6 papers
Fine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revision
Hong Yi Lin, Chunhua Liu, Haoyu Gao +2
In today's AI-assisted software engineering landscape, developers increasingly depend on LLMs that are highly capable, yet inherently imperfect. The tendency of these models to pro…
Exploring the Potential of Large Language Models in Fine-Grained Review Comment Classification
Linh Nguyen, Chunhua Liu, Hong Yi Lin +1
Code review is a crucial practice in software development. As code review nowadays is lightweight, various issues can be identified, and sometimes, they can be trivial. Research ha…
Leveraging Reviewer Experience in Code Review Comment Generation
Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude +3
Modern code review is a ubiquitous software quality assurance process aimed at identifying potential issues within newly written code. Despite its effectiveness, the process demand…
Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics
Chunhua Liu, Hong Yi Lin, Patanamon Thongtanunam
Language models have shown strong capabilities across a wide range of tasks in software engineering, such as code generation, yet they suffer from hallucinations. While hallucinati…
CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models
Hong Yi Lin, Chunhua Liu, Haoyu Gao +2
State-of-the-art large language models (LLMs) have demonstrated impressive code generation capabilities but struggle with real-world software engineering tasks, such as revising so…
Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation
Chunhua Liu, Hong Yi Lin, Patanamon Thongtanunam
Code review is an important practice in software development, yet it is time-consuming and requires substantial effort. While open-source datasets have been used to train neural mo…