从需求中提取条件自动生成验收测试：NLP方法及实例研究
Automatic Creation of Acceptance Tests by Extracting Conditionals from Requirements: NLP Approach and Case Study
摘要：
验收测试对于确定系统是否满足最终用户需求至关重要。然而，创建验收测试是一项艰巨的任务，带来了两个主要挑战：（1）从业者需要确定完全覆盖需求的正确测试用例集，以及（2）由于工具支持不足，他们需要手动创建测试用例。现有的自动生成测试用例的方法需要半形式化甚至形式化的需求符号，尽管无限制的自然语言在实践中是普遍的。在本文中，我们提出了我们的工具支持的方法CiRA（条件在要求工件）能够创建所需的测试用例的最小集从条件语句在非正式的要求。我们在与三个行业合作伙伴的案例研究中展示了CiRA的可行性。在我们的研究中，在578个手动创建的测试用例中，71.8%可以自动生成。此外，CiRA还发现了80个在手动测试用例设计中遗漏的相关测试用例。CiRA可在www.cira.bth.se/demo/上公开获取。
英文摘要：
Acceptance testing is crucial to determine whether a system fulfills end-user requirements. However, the creation of acceptance tests is a laborious task entailing two major challenges: (1) practitioners need to determine the right set of test cases that fully covers a requirement, and (2) they need to create test cases manually due to insufficient tool support. Existing approaches for automatically deriving test cases require semi-formal or even formal notations of requirements, though unrestricted natural language is prevalent in practice. In this paper, we present our tool-supported approach CiRA (Conditionals in Requirements Artifacts) capable of creating the minimal set of required test cases from conditional statements in informal requirements. We demonstrate the feasibility of CiRA in a case study with three industry partners. In our study, out of 578 manually created test cases, 71.8 % can be generated automatically. Additionally, CiRA discovered 80 relevant test cases that were missed in manual test case design. CiRA is publicly available at www.cira.bth.se/demo/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
