MINTS: A general framework and tool for supporting test-suite minimization

Abstract

Test-suite minimization techniques aim to eliminate redundant test cases from a test-suite based on some criteria, such as coverage or fault-detection capability. Most existing test-suite minimization techniques have two main limitations: they perform minimization based on a single criterion and produce suboptimal solutions. In this paper, we propose a test-suite minimization framework that overcomes these limitations by allowing testers to (1) easily encode a wide spectrum of test-suite minimization problems, (2) handle problems that involve any number of criteria, and (3) compute optimal solutions by leveraging modern integer linear programming solvers. We implemented our framework in a tool, called MINTS, that is freely-available and can be interfaced with a number of different state-of-the-art solvers. Our empirical evaluation shows that MINTS can be used to instantiate a number of different test-suite minimization problems and efficiently find an optimal solution for such problems using different solvers.