Clustering support for inadequate test suite reduction

Abstract

Regression testing is an important activity that can be expensive (e.g., for large test suites). Test suite reduction approaches speed up regression testing by removing redundant test cases. These approaches can be classified as adequate or inadequate. Adequate approaches reduce test suites so that they completely preserve the test requirements (e.g., code coverage) of the original test suites. Inadequate approaches produce reduced test suites that only partially preserve the test requirements. An inadequate approach is appealing when it leads to a greater reduction in test suite size at the expense of a small loss in fault-detection capability. We investigate a clustering-based approach for inadequate test suite reduction and compare it with well-known adequate approaches. Our investigation is founded on a public dataset and allows an exploration of trade-offs in test suite reduction. Results help a more informed decision, using guidelines defined in this research, to balance size, coverage, and fault-detection loss of reduced test suites when using clustering.