Demand-driven propagation-based strategies for testing changes
Test-suite augmentation techniques enhance test suites for software changes. In previous work, we introduced an augmentation technique that enumerates the conditions for the propagation of the effects of changes. Empirical studies showed that this technique can test changes effectively but, because of the high complexity of the technique, the experiments were small and the propagation distances from each change were limited. In this paper, we present a new, demand-driven approach for performing this propagation-based testing of changes that achieves much greater distances and that enables larger and more significant studies. We implemented this new approach and studied it on a set of changes in Java programs by comparing, to a larger extent than possible before, propagation-based strategies with other change testing techniques. Our results confirm, with statistical significance, the superiority of propagation-based strategies over other techniques, and show that these strategies are especially effective for those changes that are the most difficult to test.Copyright (c) 2013 John Wiley & Sons, Ltd.
test-suite augmentation change testing regression testing dependence analysis