Multi-Objective Test Case Selection: Local Search Approaches for the NSGA-II algorithm
Abstract
The software testing process can be very expensive and it is important to find ways in order to reduce its costs. Test case selection techniques can be used in order to reduce the amount of tests to execute and this way reducing the costs. Search algorithms are very promising approach to deal with the test case selection problem. This work proposes new hybrid algorithms for multiobjective test case selection by adding local search mechanisms into the NSGAII algorithm. The results showed that some of the mechanisms were capable of improve the NSGA-II algorithm.
References
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Elbaum, S., Malishevsky, A. G., and Rothermel, G. (2000). Prioritizing test cases for regression testing. SIGSOFT Softw. Eng. Notes, 25(5):102–112.
Harman, M. (2011). Making the case for morto: Multi objective regression test optimization. In Fourth International IEEE Conference on Software Testing, Verification and Validation, pages 111–114. IEEE Computer Society.
Papadimitriou, C. and Steiglitz, K. (1998). Combinatorial optimization: algorithms and complexity. Dover books on mathematics. Dover Publications.
Ramler, R. andWolfmaier, K. (2006). Economic perspectives in test automation - balancing automated and manual testing with opportunity cost. In Workshop on Automation of Software Test, ICSE 2006.
Webb, A. R. (2002). Statistical Pattern Recognition, 2nd Edition. John Wiley & Sons.
Published
2016-07-04
How to Cite
DE SOUZA, Luciano Soares.
Multi-Objective Test Case Selection: Local Search Approaches for the NSGA-II algorithm. In: NATIONAL COMPUTING MEETING OF FEDERAL INSTITUTES (ENCOMPIF), 3. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 760-763.
ISSN 2763-8766.
DOI: https://doi.org/10.5753/encompif.2016.9391.
