Hybrid Directed Test Suite Augmentation: An Interleaving Framework

Abstract

Test suite augmentation techniques generate test cases to cover code missed by existing regression test suites. Various augmentation techniques have been proposed, utilizing several test case generation algorithms. Research has shown that different algorithms have different strengths, and that combining them into a single hybrid approach may be cost-effective. In this paper we present a framework for hybrid test suite augmentation that allows test case generation algorithms to be interleaved dynamically and that can easily incorporate new algorithms, interleaving strategies, and choices of other parameters that influence algorithm performance. We empirically study an implementation of this framework in which we use two test case generation algorithms and several algorithm interleavings. Our results show that specific instantiations of our framework can produce augmentation techniques that are more cost-effective than others, and illustrate tradeoffs between instantiations.