Software testing is an activity to find maximum number of errors which have not been discovered yet with optimum time and effort. As the software evolves the size of the test suite and grows with new test cases being added to the test suite. However, due to time and resource constraints rerunning all the test cases in the test suite is not possible, every time the software is modified, in order to deal with these issues, the test suite size should be manageable. In this paper a novel approach is presented to select a subset of test cases that exercise the given set of requirements with for data flow testing. In order to, express the effectiveness of the proposed algorithm, both the existing Harrold Gupta and Soffa (HGS) and Bi- Objective Greedy (BOG) algorithms were applied to the generated test suites. The results obtained from the proposed algorithm were compared with the state-of-art algorithms. The results of the performance evaluation, when compared to the existing approaches show that, the proposed algorithm selects near optimal test cases that satisfy maximum number of testing requirements without compromising on the coverage aspect.
['Preethi Harris', 'Nedunchezhian Raju']
A Greedy Approach for Coverage-Based Test Suite Reduction