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An Efficient Heuristic Based Test Suite Minimization Approach
Objectives: Development of an efficient test suite minimization approach in order to reduce the size of a previously acquired test suite and produce a new representative suite which will guarantee the same requirement coverage that was achieved before minimization for an effective and efficient regression testing. Method: Test suite minimizations techniques try to reduce the size and redundancy of test suite by removing certain test cases since requirement covered by them are already covered by other test cases. But, it has been found that the acquired test cases after minimization severely lacks ability to achieve the desirable code coverage because the minimization was done based on a single test adequacy criteria. In this paper, we propose an efficient heuristic based test suite minimization algorithm which will reduce the size of the test suites with respective to multiple test adequacy criterions in order to preserve the fault detection effectiveness and code coverage characteristics of the final test suite. Findings: Our experimental results indicate that a significant percentage of reduction in the test suite size is achieved when the minimization is performed with respect to multiple test adequacy criterions. Our approach is unique compared to the existing approaches in the sense that, we carried out minimization based on multiple test adequacy criterions while most of the existing approaches usually take one or two criterions into consideration. The proposed approach is evaluated based on two well known software testing metrics; one indicate the percentage of reduction in test suite size and the second one indicate the percentage of code coverage achieved by the minimized test suite. Our experimental results indicate that a significant percentage of reduction in the size as well as significant code coverage characteristics is achieved when the minimization is done according to the proposed approach. Improvements: The important contribution of this study is that, it presents a novel and efficient test suite minimization technique that optimizes the test suite size based on multiple adequacy criterions.
Regression Testing, Software Testing, Test Data Generation, Test Suite Minimization, Test Suite Selection and Data Clustering.
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