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Regression testing has been used to support software testing activities and assure the acquirement of appropriate quality through several versions of a software program. Regression testing, however, is too expensive because it requires many test case executions, and the number of test cases increases sharply as the software evolves. In this paper, we propose the Historical Value-Based Approach, which is based on the use of historical information, to estimate the current cost and fault severity for cost-cognizant test case prioritization. We also conducted a controlled experiment to validate the proposed approach, the results of which proved the proposed approachpsilas usefulness. As a result of the proposed approach, software testers who perform regression testing are able to prioritize their test cases so that their effectiveness can be improved in terms of average percentage of fault detected per cost.