Automated unit test generation using large language models (LLMs) holds great promise but often struggles with generating tests that are both correct and maintainable in real-world projects. This paper presents KTester, a novel framework that integrates project-specific knowledge and testing domain knowledge to enhance LLM-based test generation. Our approach first extracts project structure and usage knowledge through static analysis, which provides rich context for the model. It then employs a testing-domain-knowledge-guided separation of test case design and test method generation, combined with a multi-perspective prompting strategy that guides the LLM to consider diverse testing heuristics. The generated tests follow structured templates, improving clarity and maintainability. We evaluate KTester on multiple open-source projects, comparing it against state-of-the-art LLM-based baselines using automatic correctness and coverage metrics, as well as a human study assessing readability and maintainability. Results demonstrate that KTester significantly outperforms existing methods across six key metrics, improving execution pass rate by 5.69% and line coverage by 8.83% over the strongest baseline, while requiring less time and generating fewer test cases. Human evaluators also rate the tests produced by KTester significantly higher in terms of correctness, readability, and maintainability, confirming the practical advantages of our knowledge-driven framework.

翻译：基于大语言模型（LLM）的自动化单元测试生成前景广阔，但在实际项目中生成既正确又可维护的测试用例仍面临挑战。本文提出KTester框架，通过整合项目特定知识与测试领域知识来增强基于LLM的测试生成。该方法首先通过静态分析提取项目结构与使用知识，为模型提供丰富的上下文信息；随后采用测试领域知识驱动的"测试用例设计"与"测试方法生成"分离策略，结合多视角提示机制引导LLM考虑多样化的测试启发式规则。生成的测试遵循结构化模板，显著提升可读性与可维护性。我们在多个开源项目上对KTester进行评测，采用自动化正确性与覆盖率指标以及人工可读性/可维护性评估，与最先进的基于LLM的基线方法对比。结果表明：KTester在六项关键指标上全面超越现有方法，执行通过率较最强基线提升5.69%，代码行覆盖率提升8.83%，同时所需时间更短且生成测试用例更少。人工评估者对KTester生成测试的正确性、可读性与可维护性评分显著更高，证实了知识驱动型框架的实际优势。