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Reducing Interface Mutation Costs with Multiobjective Optimization Algorithms

Reducing Interface Mutation Costs with Multiobjective Optimization Algorithms

Tiago Nobre, Silvia Regina Vergilio, Aurora Pozo
Copyright: © 2012 |Volume: 3 |Issue: 3 |Pages: 20
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781466613676|DOI: 10.4018/jncr.2012070102
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MLA

Nobre, Tiago, et al. "Reducing Interface Mutation Costs with Multiobjective Optimization Algorithms." IJNCR vol.3, no.3 2012: pp.21-40. http://doi.org/10.4018/jncr.2012070102

APA

Nobre, T., Vergilio, S. R., & Pozo, A. (2012). Reducing Interface Mutation Costs with Multiobjective Optimization Algorithms. International Journal of Natural Computing Research (IJNCR), 3(3), 21-40. http://doi.org/10.4018/jncr.2012070102

Chicago

Nobre, Tiago, Silvia Regina Vergilio, and Aurora Pozo. "Reducing Interface Mutation Costs with Multiobjective Optimization Algorithms," International Journal of Natural Computing Research (IJNCR) 3, no.3: 21-40. http://doi.org/10.4018/jncr.2012070102

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Abstract

To reduce mutation test costs, different strategies were proposed to find a set of essential operators that generates a reduced number of mutants without decreasing the mutation score. However, the operator selection is influenced by other factors, such as: number of test data, execution time, number of revealed faults, etc. In fact this is a multiobjective problem. For that, different good solutions exist. To properly deal with this problem, a selection strategy based on multiobjective algorithms was proposed and investigated for unit testing. This work explores the use of such strategy in the integration testing phase. Three multiobjective algorithms are used and evaluated with real programs: one algorithm based on tabu search (MTabu), one based on Genetic Algorithm (NSGA-II) and the third one based on Ant Colony Optimization (PACO). The results are compared with traditional strategies and contrasted with essential operators obtained in the unit testing level.

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