On the Impact of Input Models on the Fault Detection Capabilities of Combinatorial Testing

Abstract

AbstractTesting is an important activity to detect faults before software deployment. We focus on black-box combinatorial testing, where fault detection is one of the main objectives. In this paper, we argue that input model abstraction notably impacts the fault detection capability of a combinatorial test suite. First, we present experiments from previous work that support this argument. We then perform new experiments on a more diverse set of programs. These experiments use mutation testing to estimate fault detection capability, but we also include structural coverage measures in the new experiments. Finally, we elaborate on two possible improvements to obtain an optimal input abstraction strategy for not just continuous but all input domains. Both experiments suggest that input abstraction affects the fault detection capability. We claim that the improvements will produce a better input abstraction with which we can achieve better fault detection capability without increasing the test suite size.