Intelligent Local Search for Test Case Minimization

Abstract

For performing efficient regression testing, minimization of test suites is one of the primary approaches. Various kinds of test case minimization techniques have been proposed in the past, in order to do this minimization. However, due to the inherent hardness of this problem, the search for an efficient approach is still going on. In this paper, we propose the application of an intelligent local search algorithm (STAGE), for doing this optimization. The proposed approach performs local search with multiple restarts, using Hill Climbing. But the restart points for the local search are not chosen randomly, rather intelligent decisions are taken for choosing the next starting point. We have observed promising results for the selected subject programs, upon the application of this approach.