Bi-Criteria Models for All-Uses Test Suite Reduction

Abstract

Using bi-criteria decision making analysis, a new model for test suite minimization has been developed that pursues two objectives: minimizing a test suite with regard to a particular level of coverage while simultaneously maximizing error detection rates. This new representation makes it possible to achieve significant reductions in test suite size without experiencing a decrease in error detection rates. Using the all-uses inter-procedural data flow testing criterion, two binary integer linear programming models were evaluated, one a single-objective model, the other a weighted-sums bi-criteria model. The applicability of the bi-criteria model to regression test suite maintenance was also evaluated. The data show that minimization based solely on definition-use association coverage may have a negative impact on the error detection rate as compared to minimization performed with a bi-criteria model that also takes into account the ability of test cases to reveal error. Results obtained with the bi-criteria model also indicate that test suites minimized with respect to a collection of program faults are effective at revealing subsequent program faults.