Randoop-TSR: Random-based Test Generator with Test Suite Reduction
Xiangjun Liu, Yu Ping · 2022
Software testing plays a very important role in the software development process. Automated test generation tools increase the effectiveness and efficiency of software testing, and alleviate the problem of low efficiency caused by writing hand-crafted test cases. However, different test case generation methods vary in the size, code coverage, and fault detection capacity of the automatically-produced test suites. Automated test case generation tool based on random testing, Randoop as a representative, randomly and incrementally generates a large number of method sequences, which gives various possible combinations of calling methods, but the size of the test suite is not proportional to test quality. Therefore, there exists a lot of redundancy in the test cases. This paper proposes Randoop-TSR, an approach for identifying and eliminating redundant test cases on the basis of Randoop to improve the process of test generation. Our approach adopts three strategies to realize the removal of redundancy, namely: (i) similarity-based input sequence selection; (ii) redundant and duplicate assert statements elimination based on test smell detection; (iii) redundant test cases elimination without breaking test requirements (i.e., code coverage and mutation score). Randoop-TSR can eliminate redundancy effectively, and greatly reduce the size of test suites and execution time. Furthermore, our approach improves the efficiency and understandability of test cases while retaining code coverage and mutation score.