Improved Extremal Optimization for Constrained Pairwise Testing

Abstract

Pairwise testing, which requires that every combination of valid values of each pair of system factors be covered by at lease one test case, plays an important role in software testing since many faults are caused by unexpected 2-way interactions among system factors. In real systems, constraints usually exist between values, which means that some values cannot coexist in a valid test. Although meta-heuristic strategies like simulated annealing can generally discover smaller pairwise test suite in the presence of constraints, they may cost more time to perform search, compared with greedy algorithms. We propose a new method, improved extremal optimization, for constructing constrained pairwise test suites. Experimental results show that improved extremal optimization gives similar size of resulting pairwise test suite and yields a 13% reduction in solution time over simulated annealing.