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What Types of Code Review Comments Do Developers Most Frequently Resolve?
Saul Goldman, Hong Yi Lin, Jirat Pasuksmit +11
Large language model (LLM)-powered code review automation tools have been introduced to generate code review comments. However, not all generated comments will drive code changes.…
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…
AI Failures in the Eyes of the Downstream Developer: A First Look at Concerns, Practices, and Challenges
Haoyu Gao, Mansooreh Zahedi, Wenxin Jiang +3
With the advancement of AI models, more software systems are adopting AI as a component to facilitate automation. Pre-trained models (PTMs) have become a cornerstone of AI-based so…
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…
Should Code Models Learn Pedagogically? A Preliminary Evaluation of Curriculum Learning for Real-World Software Engineering Tasks
Kyi Shin Khant, Hong Yi Lin, Patanamon Thongtanunam
Learning-based techniques, especially advanced pre-trained models for code have demonstrated capabilities in code understanding and generation, solving diverse software engineering…
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…