UnitTenX: Generating Tests for Legacy Packages with AI Agents Powered by Formal Verification
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912632540233728 |
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| author | Charalambous, Yiannis Coelho Jr, Claudionor N. Lamb, Luis Cordeiro, Lucas C. |
| author_facet | Charalambous, Yiannis Coelho Jr, Claudionor N. Lamb, Luis Cordeiro, Lucas C. |
| contents | This paper introduces UnitTenX, a state-of-the-art open-source AI multi-agent system designed to generate unit tests for legacy code, enhancing test coverage and critical value testing. UnitTenX leverages a combination of AI agents, formal methods, and Large Language Models (LLMs) to automate test generation, addressing the challenges posed by complex and legacy codebases. Despite the limitations of LLMs in bug detection, UnitTenX offers a robust framework for improving software reliability and maintainability. Our results demonstrate the effectiveness of this approach in generating high-quality tests and identifying potential issues. Additionally, our approach enhances the readability and documentation of legacy code. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05441 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UnitTenX: Generating Tests for Legacy Packages with AI Agents Powered by Formal Verification Charalambous, Yiannis Coelho Jr, Claudionor N. Lamb, Luis Cordeiro, Lucas C. Software Engineering Artificial Intelligence This paper introduces UnitTenX, a state-of-the-art open-source AI multi-agent system designed to generate unit tests for legacy code, enhancing test coverage and critical value testing. UnitTenX leverages a combination of AI agents, formal methods, and Large Language Models (LLMs) to automate test generation, addressing the challenges posed by complex and legacy codebases. Despite the limitations of LLMs in bug detection, UnitTenX offers a robust framework for improving software reliability and maintainability. Our results demonstrate the effectiveness of this approach in generating high-quality tests and identifying potential issues. Additionally, our approach enhances the readability and documentation of legacy code. |
| title | UnitTenX: Generating Tests for Legacy Packages with AI Agents Powered by Formal Verification |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2510.05441 |