Design and Analysis of Cost-Cognizant Test Case Prioritization Using Genetic Algorithm with Test History

During software development, regression testing is usually used to assure the quality of modified software. The techniques of test case prioritization schedule the test cases for regression testing in an order that attempts to increase the effectiveness in accordance with some performance goal. The most general goal is the rate of fault detection. It assumes all test case costs and fault severities are uniform. However, those factors usually vary. In order to produce a more satisfactory order, the cost-cognizant metric that incorporates varying test case costs and fault severities is proposed. In this paper, we propose a cost-cognizant test case prioritization technique based on the use of historical records and a genetic algorithm. We run a controlled experiment to evaluate the proposed technique's effectiveness. Experimental results indicate that our proposed technique frequently yields a higher Average Percentage of Faults Detected per Cost (APFDc). The results also show that our proposed technique is also useful in terms of APFDc when all test case costs and fault severities are uniform.