AILinkPreviewer: Enhancing Code Reviews with LLM-Powered Link Previews
Issued Date
2025-01-01
Resource Type
ISSN
15301362
Scopus ID
2-s2.0-105035208053
Journal Title
Proceedings Asia Pacific Software Engineering Conference APSEC
Start Page
1021
End Page
1024
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings Asia Pacific Software Engineering Conference APSEC (2025) , 1021-1024
Suggested Citation
Trakoolgerntong P., Xiao T., Kondo M., Ragkhitwetsagul C., Choetkiertikul M., Sangaroonsilp P., Kamei Y. AILinkPreviewer: Enhancing Code Reviews with LLM-Powered Link Previews. Proceedings Asia Pacific Software Engineering Conference APSEC (2025) , 1021-1024. 1024. doi:10.1109/APSEC66846.2025.00121 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116230
Title
AILinkPreviewer: Enhancing Code Reviews with LLM-Powered Link Previews
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Corresponding Author(s)
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Abstract
Code review is a key practice in software engineering, where developers evaluate code changes to ensure quality and maintainability. Links to issues and external resources are often included in Pull Requests (PRs) to provide additional context, yet they are typically discarded in automated tasks such as PR summarization and code review comment generation. This limits the richness of information available to reviewers and increases cognitive load by forcing context-switching. To address this gap, we present AILINKPREVIEWER, a tool that leverages Large Language Models (LLMs) to generate previews of links in PRs using PR metadata, including titles, descriptions, comments, and link body content. We analyzed 50 engineered GitHub repositories and compared three approaches: Contextual LLM summaries, Non-Contextual LLM summaries, and Metadata-based previews. The results in metrics such as BLEU, BERTScore, and compression ratio show that contextual summaries consistently outperform other methods. However, in a user study with seven participants, most preferred non-contextual summaries, suggesting a trade-off between metric performance and perceived usability. These findings demonstrate the potential of LLM-powered link previews to enhance code review efficiency and to provide richer context for developers and automation in software engineering. The video demo is available at https://www.youtube.com/ watch?v=h2qH4RtrB3E, and the tool and its source code can be found at https://github.com/c4rtune/AILinkPreviewer.