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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2512.17540 |
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| _version_ | 1866910001582309376 |
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| author | Wang, Kai Mao, Bingcheng Jia, Shuai Ding, Yujie Han, Dongming Ma, Tianyi Cao, Bin |
| author_facet | Wang, Kai Mao, Bingcheng Jia, Shuai Ding, Yujie Han, Dongming Ma, Tianyi Cao, Bin |
| contents | Automating code review with Large Language Models (LLMs) shows immense promise, yet practical adoption is hampered by their lack of reliability, context-awareness, and control. To address this, we propose Specification-Grounded Code Review (SGCR), a framework that grounds LLMs in human-authored specifications to produce trustworthy and relevant feedback. SGCR features a novel dual-pathway architecture: an explicit path ensures deterministic compliance with predefined rules derived from these specifications, while an implicit path heuristically discovers and verifies issues beyond those rules. Deployed in a live industrial environment at HiThink Research, SGCR's suggestions achieved a 42% developer adoption rate-a 90.9% relative improvement over a baseline LLM (22%). Our work demonstrates that specification-grounding is a powerful paradigm for bridging the gap between the generative power of LLMs and the rigorous reliability demands of software engineering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_17540 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SGCR: A Specification-Grounded Framework for Trustworthy LLM Code Review Wang, Kai Mao, Bingcheng Jia, Shuai Ding, Yujie Han, Dongming Ma, Tianyi Cao, Bin Software Engineering Automating code review with Large Language Models (LLMs) shows immense promise, yet practical adoption is hampered by their lack of reliability, context-awareness, and control. To address this, we propose Specification-Grounded Code Review (SGCR), a framework that grounds LLMs in human-authored specifications to produce trustworthy and relevant feedback. SGCR features a novel dual-pathway architecture: an explicit path ensures deterministic compliance with predefined rules derived from these specifications, while an implicit path heuristically discovers and verifies issues beyond those rules. Deployed in a live industrial environment at HiThink Research, SGCR's suggestions achieved a 42% developer adoption rate-a 90.9% relative improvement over a baseline LLM (22%). Our work demonstrates that specification-grounding is a powerful paradigm for bridging the gap between the generative power of LLMs and the rigorous reliability demands of software engineering. |
| title | SGCR: A Specification-Grounded Framework for Trustworthy LLM Code Review |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2512.17540 |