Uma abordagem evolucionária para o teste de instruções select SQL com o uso da análise de mutantes

• Ano de defesa: 2013
• Autor(a) principal: Monção, Ana Claudia Bastos Loureiro
• Orientador(a): Camilo Júnior, Celso Gonçalves
• Banca de defesa: Camilo Júnior, Celso Gonçalves, Leitão Júnior, Plínio de Sá, Rodrigues, Cássio Leonardo, Souza, Jerffeson Teixeira de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• 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: Teste de software; Análise de mutantes; Algoritmos genéticos
• Palavras-chave em Inglês: Search - based software testing; Search-based software engineering; Software testing; Mutation analisys; Genetic algorithms
• Área do conhecimento CNPq: CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/3346

Software Testing is an important area of Software Engineering to ensuring the software quality. It consists of activities that involve long time and high costs, but need to be made throughout the process of building software. As in other areas of software engineering, there are problems in the activities of Software Testing whose solution is not trivial. For these problems, several techniques of optimization and search have been explored trying to find an optimal solution or near optimal, giving rise to lines of research textit Search-Based Software Engineering (SBSE) and textit Search-Based Software Testing (SBST). This work is part of this context and aims to solve the problem of selecting test data for test execution in SQL statements. Given the number of potential solutions to this problem, the proposed approach combines techniques Mutation Analysis for SQL with Evolutionary Computation to find a reduced data set, that be able to detect a large number of defects in SQL statements of a particular application. Based on a heuristic perspective, the proposal uses Genetic Algorithms (GA) to select tuples from a existing database (from production environment) trying to reduce it to a set of data relevant and effective. During the evolutionary process, Mutation Analysis is used to evaluate each set of test data selected by the AG. The results obtained from the experiments showed a good performance using meta-heuristic of Genetic Algorithms, and its variations.