Impact and Performance of Randomized Test Generation Using Prolog

Abstract We study randomized generation of sequences of test inputs to a system using Prolog. Prolog is a natural fit to generate test sequences that have complex logical interdependent structure. To counter the problems posed by a large (or infinite) set of possible tests, randomization is a natural choice. We study the impact that randomization in conjunction with SLD resolution have on the test performance. To this end, this paper proposes two strategies to add randomization to a test-generating program. One strategy works on top of standard Prolog semantics, whereas the other alters the SLD selection function. We analyze the mean time to reach a test case and the mean number of generated test cases in the framework of Markov chains. Finally, we provide an additional empirical evaluation and comparison between both approaches.