用AI代理+形式化验证自动生成旧代码单元测试，提升覆盖率与可靠性。
UnitTenX: Generating Tests for Legacy Packages with AI Agents Powered by Formal Verification
- 构建多智能体系统，融合LLM与形式化方法生成测试
- 在真实项目中提升测试覆盖率，有效识别潜在缺陷
- 适合维护老旧系统或需高可靠性的工程团队
本文提出UnitTenX，一个开源的先进AI多智能体系统，用于为遗留代码生成单元测试，以提高测试覆盖率和关键路径测试能力。该系统结合AI智能体、形式化方法与大型语言模型（LLMs），自动完成测试生成，应对复杂且老旧代码库带来的挑战。尽管LLMs在漏洞检测方面存在局限，UnitTenX仍提供了一个强大框架，显著提升软件的可靠性与可维护性。实验结果表明，该方法能生成高质量测试，并有效发现潜在问题。此外，该方法还增强了遗留代码的可读性与文档质量。
原文摘要 · Abstract (English)
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.
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