Abstract
Test suite minimisation techniques seek to reduce the effort required for regression testing by selecting a subset of test suites. In previous work, the problem has been considered as a single-objective optimisation problem. However, real world regression testing can be a complex process in which multiple testing criteria and constraints are involved. This paper presents the concept of Pareto efficiency for the test suite minimisation problem. The Pareto-efficient approach is inherently capable of dealing with multiple objectives, providing the decision maker with a group of solutions that are not dominated by each other. The paper illustrates the benefits of Pareto efficient multi-objective test suite minimisation with empirical studies of two and three objective formulations, in which multiple objectives such as coverage and past fault-detection history are considered. The paper utilises a hybrid, multi-objective genetic algorithm that combines the efficient approximation of the greedy approach with the capability of population based genetic algorithm to produce higher-quality Pareto fronts. (C) 2009 Elsevier Inc. All rights reserved.
| Original language | English |
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| Pages (from-to) | 689 - 701 |
| Number of pages | 13 |
| Journal | Journal of Systems and Software |
| Volume | 83 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Apr 2010 |