Text Similarity Analysis for Test Suite Minimization

Sammanfattning: Software testing is the most expensive phase in the software development life cycle. It is thus understandable why test optimization is a crucial area in the software development domain. In software testing, the gradual increase of test cases demands large portions of testing resources (budget and time). Test Suite Minimization is considered a potential approach to deal with the test suite size problem. Several test suite minimization techniques have been proposed to efficiently address the test suite size problem. Proposing a good solution for test suite minimization is a challenging task, where several parameters such as code coverage, requirement coverage, and testing cost need to be considered before removing a test case from the testing cycle. This thesis proposes and evaluates two different NLP-based approaches for similarity analysis between manual integration test cases, which can be employed for test suite minimization. One approach is based on syntactic text similarity analysis and the other is a machine learning based semantic approach. The feasibility of the proposed solutions is studied through analysis of industrial use cases at Ericsson AB in Sweden. The results show that the semantic approach barely manages to outperform the syntactic approach. While both approaches show promise, subsequent studies will have to be done to further evaluate the semantic similarity based method.