用大模型自动识别代码变更后需维护的测试用例。
Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes
- 构建多智能体系统，基于代码变更预测需维护的测试用例。
- 在爱立信实际场景中验证，可减少测试维护成本与人工干预。
- 提出工业级部署考量，适配企业级测试流程优化需求。
软件测试中的大量成本和精力消耗于测试维护——即为保持测试套件与被测系统同步或提升其质量，对测试用例进行增删改。工具支持可通过自动化或提供指导来降低此类成本并提升质量。本研究探索大型语言模型（LLMs）在测试维护中的能力与应用。我们在爱立信公司开展案例研究，分析触发测试维护的信号、LLM可执行的操作，以及在工业环境中部署时的关键考量。我们还提出并演示了一种多智能体架构，可在代码变更后预测哪些测试需要维护。这些成果共同推进了对如何在工业测试维护中部署LLM的理论与实践理解。
原文摘要 · Abstract (English)
Much of the cost and effort required during the software testing process is invested in performing test maintenance - the addition, removal, or modification of test cases to keep the test suite in sync with the system-under-test or to otherwise improve its quality. Tool support could reduce the cost - and improve the quality - of test maintenance by automating aspects of the process or by providing guidance and support to developers. In this study, we explore the capabilities and applications of large language models (LLMs) - complex machine learning models adapted to textual analysis - to support test maintenance. We conducted a case study at Ericsson AB where we explore the triggers that indicate the need for test maintenance, the actions that LLMs can take, and the considerations that must be made when deploying LLMs in an industrial setting. We also propose and demonstrate a multi-agent architecture that can predict which tests require maintenance following a change to the source code. Collectively, these contributions advance our theoretical and practical understanding of how LLMs can be deployed to benefit industrial test maintenance processes.
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