用大模型自动写测试、跑用例并生成报告，提升效率。
The Potential of LLMs in Automating Software Testing: From Generation to Reporting
- 构建基于大模型的智能测试代理，自动完成测试生成与执行。
- 在多款 Python 和 Java 应用上实现高覆盖率，运行高效。
- 适合希望减少人工测试成本的开发团队和自动化测试研究者。
高质量软件对软件工程至关重要，需通过有效的验证与确认流程保障。尽管手动测试有效，但耗时且成本高，促使自动化方法需求上升。大型语言模型（LLMs）近年来显著影响软件工程，尤其在需求分析、测试自动化与调试方面。本文提出一种面向代理的自动化测试框架，利用 LLMs 减少人工干预，提升测试效率。该框架集成 LLMs 实现单元测试生成、调用图可视化以及测试执行与报告自动化。在多个 Python 与 Java 应用上的评估表明，系统具备高测试覆盖率且运行高效。研究凸显了大模型驱动的智能体在优化软件测试流程中的潜力，同时应对可扩展性与准确性挑战。
原文摘要 · Abstract (English)
Having a high quality software is essential in software engineering, which requires robust validation and verification processes during testing activities. Manual testing, while effective, can be time consuming and costly, leading to an increased demand for automated methods. Recent advancements in Large Language Models (LLMs) have significantly influenced software engineering, particularly in areas like requirements analysis, test automation, and debugging. This paper explores an agent-oriented approach to automated software testing, using LLMs to reduce human intervention and enhance testing efficiency. The proposed framework integrates LLMs to generate unit tests, visualize call graphs, and automate test execution and reporting. Evaluations across multiple applications in Python and Java demonstrate the system's high test coverage and efficient operation. This research underscores the potential of LLM-powered agents to streamline software testing workflows while addressing challenges in scalability and accuracy.
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