FTMES@r: um método de localização de defeitos baseado em estratégias de execução de mutantes

• Ano de defesa: 2018
• Autor(a) principal: Oliveira, André Assis Lôbo de
• Orientador(a): Camilo Júnior, Celso Gonçalves
• Banca de defesa: Camilo Júnior, Celso Gonçalve, Vincenzi, Auri Marcelo Rizzo, Rodrigues, Cássio Leonardo, Freitas, Eduardo Noronha de Andrade Freitas, Leitão, Plínio de Sá
• Tipo de documento: Tese
• 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; Depuração; Localização de defeitos baseada no espectro do programa; Localização de defeitos baseada em mutação; Estratégias de execução de mutantes
• Palavras-chave em Inglês: Software testing; Spectrum-based fault localization; Mutation-based fault localization; Mutation execution strategies
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/9194

Fault localization has been one of the most manual and costly software debugging activities. The spectrum-based fault localization is the most studied and evaluated fault localization approach. Mutation-based fault localization is a promising approach to the efficacy of localization but with a high computational cost due to the executions between test cases and programs mutants. In this context, this thesis purposes FTMES@r: a fault localization method to reduce the computational MBFL cost while maintaining the efficacy of localization. Differing from all reduction techniques, FTMES@r optimizes two stages: i) the selection of program elements (SFilter@r) and ii) the execution of the mutants (FTMES). The SFilter@r component uses the accuracy of the SBFL approach in forming a smaller ranking by selecting the program elements up to a given position @r of the ranking of all elements. Thus, SFilter@r employs the first level of cost reduction of MBFL because the generation of mutants considers only the program elements of this reduced rank. In the mutants execution stage, the Failed-Test-Oriented Mutant Execution Strategy (FTMES) component applies the second level of cost reduction by running mutants only with the set of failed test cases (Tf) and using the mutants with the set of test cases that pass (Tp). The experimentation comprises a comparison of 10 localization techniques, 221 real defects, and 6 evaluation metrics. The results show that FTMES@r presents the best cost-benefit relationship among the studied techniques.