Uma abordagem coevolucionária para seleção de casos de teste e programas mutantes no contexto do teste de mutação
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Data
2013-12-05
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Universidade Federal de Goiás
Resumo
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.
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Citação
OLIVEIRA, André Assis Lôbo de. Uma abordagem coevolucionária para seleção de casos de teste e programas mutantes no contexto do teste de mutação. 2013. 158 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2013.