Software testing is still a manual process in many industries, despite the recent improvements in automated testing techniques. As a result, test cases (which consist of one or more test steps that need to be executed manually by the tester) are often specified in natural language by different employees and many redundant test cases might exist in the test suite. This increases the (already high) cost of test execution. Manually identifying similar test cases is a time-consuming and error-prone task. Therefore, in this paper, we propose an unsupervised approach to identify similar test cases. Our approach uses a combination of text embedding, text similarity and clustering techniques to identify similar test cases. We evaluate five different text embedding techniques, two text similarity metrics, and two clustering techniques to cluster similar test steps and three techniques to identify similar test cases from the test step clusters. Through an evaluation in an industrial setting, we showed that our approach achieves a high performance to cluster test steps (an F-score of 87.39%) and identify similar test cases (an F-score of 86.13%). Furthermore, a validation with developers indicates several different practical usages of our approach (such as identifying redundant test cases), which help to reduce the testing manual effort and time.

中文翻译：

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识别以自然语言指定的相似测试用例

尽管最近自动化测试技术有所改进，但在许多行业中，软件测试仍然是一个手动过程。因此，测试用例（由一个或多个测试步骤组成，需要测试人员手动执行）通常由不同的员工以自然语言指定，并且测试套件中可能存在许多冗余测试用例。这增加了（已经很高的）测试执行成本。手动识别相似的测试用例是一项耗时且容易出错的任务。因此，在本文中，我们提出了一种无监督的方法来识别相似的测试用例。我们的方法结合使用文本嵌入、文本相似性和聚类技术来识别相似的测试用例。我们评估了五种不同的文本嵌入技术，两种文本相似性度量，以及两种聚类技术来聚类相似的测试步骤和三种技术来从测试步骤集群中识别相似的测试用例。通过在工业环境中进行的评估，我们表明我们的方法在聚类测试步骤（F 分数为 87.39%）和识别相似测试用例（F 分数为 86.13%）方面实现了高性能。此外，与开发人员的验证表明我们的方法有几种不同的实际用途（例如识别冗余测试用例），这有助于减少测试手动工作量和时间。