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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2403.14274 |
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| _version_ | 1866911880753184768 |
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| author | Mao, Zhenyu Li, Jialong Jin, Dongming Li, Munan Tei, Kenji |
| author_facet | Mao, Zhenyu Li, Jialong Jin, Dongming Li, Munan Tei, Kenji |
| contents | Recent advancements in large language models (LLMs) have highlighted the potential for vulnerability detection, a crucial component of software quality assurance. Despite this progress, most studies have been limited to the perspective of a single role, usually testers, lacking diverse viewpoints from different roles in a typical software development life-cycle, including both developers and testers. To this end, this paper introduces a multi-role approach to employ LLMs to act as different roles simulating a real-life code review process and engaging in discussions toward a consensus on the existence and classification of vulnerabilities in the code. Preliminary evaluation of this approach indicates a 13.48% increase in the precision rate, an 18.25% increase in the recall rate, and a 16.13% increase in the F1 score. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14274 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-role Consensus through LLMs Discussions for Vulnerability Detection Mao, Zhenyu Li, Jialong Jin, Dongming Li, Munan Tei, Kenji Software Engineering Artificial Intelligence Recent advancements in large language models (LLMs) have highlighted the potential for vulnerability detection, a crucial component of software quality assurance. Despite this progress, most studies have been limited to the perspective of a single role, usually testers, lacking diverse viewpoints from different roles in a typical software development life-cycle, including both developers and testers. To this end, this paper introduces a multi-role approach to employ LLMs to act as different roles simulating a real-life code review process and engaging in discussions toward a consensus on the existence and classification of vulnerabilities in the code. Preliminary evaluation of this approach indicates a 13.48% increase in the precision rate, an 18.25% increase in the recall rate, and a 16.13% increase in the F1 score. |
| title | Multi-role Consensus through LLMs Discussions for Vulnerability Detection |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2403.14274 |