Software testing aims to assure the quality of software under test. To improve the efficiency of software testing, especially regression testing, test-case prioritization is proposed to schedule the execution order of test cases in software testing. Among various test-case prioritization techniques, the simple additional coverage-based technique, which is a greedy strategy, achieves surprisingly competitive empirical results. To investigate how much difference there is between the order produced by the additional technique and the optimal order in terms of coverage, we conduct a study on various empirical properties of optimal coverage-based test-case prioritization. To enable us to achieve the optimal order in acceptable time for our object programs, we formulate optimal coverage-based test-case prioritization as an integer linear programming (ILP) problem. Then we conduct an empirical study for comparing the optimal technique with the simple additional coverage-based technique. From this empirical study, the optimal technique can only slightly outperform the additional coverage-based technique with no statistically significant difference in terms of coverage, and the latter significantly outperforms the former in terms of either fault detection or execution time. As the optimal technique schedules the execution order of test cases based on their structural coverage rather than detected faults, we further implement the ideal optimal test-case prioritization technique, which schedules the execution order of test cases based on their detected faults. Taking this ideal technique as the upper bound of test-case prioritization, we conduct another empirical study for comparing the optimal technique and the simple additional technique with this ideal technique. From this empirical study, both the optimal technique and the additional technique significantly outperform the ideal technique in terms of coverage, but the latter significantly outperforms the former two techniques in terms of fault detection. Our findings indicate that researchers may need take cautions in pursuing the optimal techniques in test-case prioritization with intermediate goals.

Most test suite reduction techniques aim to select, from a given test suite, a minimal representative subset of test cases that retains the same code coverage as the suite. Empirical studies have shown, however, that test suites reduced in this manner may lose fault detection capability. Techniques have been proposed to retain certain redundant test cases in the reduced test suite so as to reduce the loss in fault-detection capability, but these still do concede some degree of loss. Thus, these techniques may be applicable only in cases where loose demands are placed on the upper limit of loss in fault-detection capability. In this work we present an on-demand test suite reduction approach, which attempts to select a representative subset satisfying the same test requirements as an initial test suite conceding at most l% loss in fault-detection capability for at least c% of the instances in which it is applied. Our technique collects statistics about loss in fault-detection capability at the level of individual statements and models the problem of test suite reduction as an integer linear programming problem. We have evaluated our approach in the contexts of three scenarios in which it might be used. Our results show that most test suites reduced by our approach satisfy given fault detection capability demands, and that the approach compares favorably with an existing test suite reduction approach.