Uma abordagem coevolucionária para seleção de casos de teste e programas mutantes no contexto do teste de mutação

• Ano de defesa: 2013
• Autor(a) principal: Oliveira, André Assis Lôbo de
• Orientador(a): Camilo Junior, Celso Gonçalves
• Banca de defesa: Maldonado, José Carlos, Vincenzi, Auri Marcelo Rizzo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• dARK ID: ark:/38995/0013000000579
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RG)
• País: Brasil
• Palavras-chave em Português: Seleção de casos de teste; Metaheurística; Algoritmos genéticos (AGs); Efetividade genética; Teste de mutação
• Palavras-chave em Inglês: Search based software testing (SBST); Test case selection; Metaheuristics; Coevolutionary genetic algorithm; Genetic effectiveness; Mutation testing
• Área do conhecimento CNPq: CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/3298

Verification and Validation Activities (V&V) consume about 50% to 60% of the total cost of a software lifecycle. Among those activities, Software Testing technique is one which is mostly used during this process. One of the main problems related to detected in Software Testing is to find a set of tests (subset from input domain of the problem) which is effective to detect the remaining bugs in the software. The Search-Based Software Testing (SBST) approach uses metaheuristics to find low cost set of tests with a high effectiveness to detect bugs. From several existing test criteria, Mutation Testing is considered quite promising to reveal bugs, despite its high computational cost, due to the great quantity of mutant programs generated. Therefore, this dissertation addresses the problem of selecting mutant programs and test cases in Mutation Testing context. To this end, it is proposed a Coevolutionary Genetic Algorithm (CGA) and the concept of Genetic Effectiveness, implemented by Genetic Classification (GC) and new genetic operators adapted to the proposed representation. Furthermore, the Genetic Algorithm Coevolutionary with Controlled Genetic Classification (CGACGCop) is proposed for improving the efficiency of CGA’s GC. The CGA is applied in three categories of benchmarks and compared to other five methods. The results show a better performance of the CGA in subsets selection with better mutation score, as well as improvement of CGACGCop in use of GC. These results evidence the proposal approach with promising use in the context of Mutation Testing.