Algoritmo Genético Aplicado à Geração Automática de Casos de Teste
Resumo
Testes são uma parte essencial do desenvolvimento de software e, neste sentido, este trabalho propõe um framework para comparação de algoritmos na tarefa de geração de casos de teste e posterior comparação desses resultados para Algoritmos Genéticos, Algoritmos Genéticos com Interação Social e Hill Climbing.Referências
Harman, M. and McMinn, P. (2010). A theoretical and empirical study of search-based testing: Local, global, and hybrid search. Software Engineering, IEEE Transactions on, 36:226 – 247.
Holland, J. (1975). An introductory analysis with applications to biology, control, and artificial intelligence. Adaptation in Natural and Artificial Systems. First Edition, The University of Michigan, USA.
Lo, F.-Y., Chen, C.-H., and Chen, Y.-p. (2019). Genetic algorithms as shrinkers in property-based testing. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO ’19, page 291–292, New York, NY, USA. Association for Computing Machinery.
MacIver, D., Hatfield-Dodds, Z., and Contributors, M. (2019). Hypothesis: A new approach to property-based testing. Journal of Open Source Software, 4:1891.
Pereira, R. L., Souza, D. L., Mollinetti, M. A. F., Serra Neto, M. T. R., Yasojima, E. K. K., Teixeira, O. N., and De Oliveira, R. C. L. (2020). Game theory and social interaction for selection and crossover pressure control in genetic algorithms: An empirical analysis to real-valued constrained optimization. IEEE Access, 8:144839–144865.
Ramler, R. and Wolfmaier, K. (2006). Economic perspectives in test automation: Balancing automated and manual testing with opportunity cost. pages 85–91.
Sharma, A., Patani, R., and Aggarwal, A. (2016). Software testing using genetic algorithms. International Journal of Computer Science & Engineering Survey, 7:21–33.
Sommerville, I. (2011). Engenharia de software. Pearson Prentice Hall.
Sonkar, S. K., Malviya, A., Gupta, D., and Chandra, G. (2012). Software testing using genetic algorithm.
Tracey, N., Clark, J., Mander, K., and Mcdermid, J. (2000). Automated test-data generation for exception conditions. Softw., Pract. Exper., 30.
Holland, J. (1975). An introductory analysis with applications to biology, control, and artificial intelligence. Adaptation in Natural and Artificial Systems. First Edition, The University of Michigan, USA.
Lo, F.-Y., Chen, C.-H., and Chen, Y.-p. (2019). Genetic algorithms as shrinkers in property-based testing. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO ’19, page 291–292, New York, NY, USA. Association for Computing Machinery.
MacIver, D., Hatfield-Dodds, Z., and Contributors, M. (2019). Hypothesis: A new approach to property-based testing. Journal of Open Source Software, 4:1891.
Pereira, R. L., Souza, D. L., Mollinetti, M. A. F., Serra Neto, M. T. R., Yasojima, E. K. K., Teixeira, O. N., and De Oliveira, R. C. L. (2020). Game theory and social interaction for selection and crossover pressure control in genetic algorithms: An empirical analysis to real-valued constrained optimization. IEEE Access, 8:144839–144865.
Ramler, R. and Wolfmaier, K. (2006). Economic perspectives in test automation: Balancing automated and manual testing with opportunity cost. pages 85–91.
Sharma, A., Patani, R., and Aggarwal, A. (2016). Software testing using genetic algorithms. International Journal of Computer Science & Engineering Survey, 7:21–33.
Sommerville, I. (2011). Engenharia de software. Pearson Prentice Hall.
Sonkar, S. K., Malviya, A., Gupta, D., and Chandra, G. (2012). Software testing using genetic algorithm.
Tracey, N., Clark, J., Mander, K., and Mcdermid, J. (2000). Automated test-data generation for exception conditions. Softw., Pract. Exper., 30.
Publicado
18/11/2021
Como Citar
NETO, Adilson de Almeida; PEREIRA, Rodrigo Lisbôa; OLIVEIRA, Roberto Célio Limão de.
Algoritmo Genético Aplicado à Geração Automática de Casos de Teste. In: ESCOLA REGIONAL DE ALTO DESEMPENHO NORTE 2 (ERAD-NO2) E ESCOLA REGIONAL DE APRENDIZADO DE MÁQUINA E INTELIGÊNCIA ARTIFICIAL NORTE 2 (ERAMIA-NO2), 1. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 37-40.
DOI: https://doi.org/10.5753/erad-no2.2021.18678.
