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Software testing is still a manual process in many industries, despite therecent improvements in automated testing techniques. As a result, test casesare often specified in natural language by different employees and manyredundant test cases might exist in the test suite. This increases the (alreadyhigh) cost of test execution. Manually identifying similar test cases is atime-consuming and error-prone task. Therefore, in this paper, we propose anunsupervised approach to identify similar test cases. Our approach uses acombination of text embedding, text similarity and clustering techniques toidentify similar test cases. We evaluate five different text embeddingtechniques, two text similarity metrics, and two clustering techniques tocluster similar test steps and four techniques to identify similar test casesfrom the test step clusters. Through an evaluation in an industrial setting, weshowed that our approach achieves a high performance to cluster test steps (anF-score of 87.39%) and identify similar test cases (an F-score of 83.47%).Furthermore, a validation with developers indicates several different practicalusages of our approach (such as identifying redundant and legacy test cases),which help to reduce the testing manual effort and time.

Identifying Similar Test Cases That Are Specified in Natural Language