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Regression testing is an expensive activity that can account for a large proportion of the software maintenance budget. Because engineers add tests into test suites as software evolves, over time, increased test suite size makes revalidation of the software more expensive. Regression test selection, test suite reduction, and test case prioritization techniques can help with this, by reducing the number of regression tests that must be run and by helping testers meet testing objectives more quickly. These techniques, however can be expensive to employ and may not reduce overall regression testing costs. Thus, practitioners and researchers could benefit from cost models that would help them assess the cost-benefits of techniques. Cost models have been proposed for this purpose, but some of these models omit important factors, and others cannot truly evaluate cost-effectiveness. In this paper, we present new cost-benefits models for regression test selection, test suite reduction, and test case prioritization, that capture previously omitted factors, and support cost-benefits analyses where they were not supported before. We present the results of an empirical study assessing these models.