论文标题
通过从要求中提取条件：NLP方法和案例研究来自动创建接受测试
Automatic Creation of Acceptance Tests by Extracting Conditionals from Requirements: NLP Approach and Case Study
论文作者
论文摘要
接受测试对于确定系统是否满足最终用户要求至关重要。但是，建立接受测试是一项艰巨的任务，需要两个主要的挑战：（1）从业人员需要确定完全满足要求的正确测试用例，并且（2）由于工具支持不足，他们需要手动创建测试案例。现有的自动得出测试案例的方法需要半正式甚至正式的要求表示，尽管在实践中不受限制的自然语言很普遍。在本文中，我们介绍了我们的工具支持的方法CIRA（需求伪像的条件），能够从非正式要求中的条件陈述中创建最小的必需测试用例集。我们在与三个行业合作伙伴的案例研究中证明了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，可以自动生成71.8％。此外，CIRA还发现了80个相关的测试用例，这些案例在手动测试案例设计中遗漏了。 CIRA可在www.cira.bth.se/demo/上公开获取。
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/.
