Multi-Objective Test Case Selection: Local Search Approaches for the NSGA-II algorithm
Resumo
O processo de testes de software pode ser bastante caro, portanto é importante se encontrar formas de reduzir os custos desse processo. Técnicas de seleção de casos de teste podem ser usadas de forma a reduzir a quantidade de testes a executar e dessa forma reduzir os custos. A utilização de algoritmos de busca são uma maneira promissora de resolver o problema de seleção de casos de teste. Esse trabalho propõe novos algoritmos híbridos de busca para seleção multiobjetivo de casos de teste através da inserção de mecanismos de busca local no algoritmo NSGA-II. Os resultados mostraram que alguns dos mecanismos foram capazes the introduzir melhoras no algoritmo NSGA-II.
Referências
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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.
Publicado
04/07/2016
Como Citar
DE SOUZA, Luciano Soares.
Multi-Objective Test Case Selection: Local Search Approaches for the NSGA-II algorithm. In: ENCONTRO NACIONAL DE COMPUTAÇÃO DOS INSTITUTOS FEDERAIS (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.
