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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Assis Lobo de Oliveira, A. Gonyalves Camilo-Junior, C. Vincenzi, A.M.R. |
| Copyright Year | 2013 |
| Description | Author affiliation: Inst. de Inf., Univ. Fed. de Goias, Goiania, Brazil (Assis Lobo de Oliveira, A.; Gonyalves Camilo-Junior, C.; Vincenzi, A.M.R.) |
| Abstract | One of the main problems to perform the 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 paper 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, describing a new representation and implementing new genetic operators. The CGA is applied in five benchmarks and the results are compared to other five methods, showing a better performance of the proposed algorithm in subsets automatic selection with better mutation score and greater reduction of computational cost, specifically the amount of testing, when compared with exhaustive test. |
| Starting Page | 829 |
| Ending Page | 836 |
| File Size | 2507874 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479904532 |
| e-ISBN | 9781479904549 |
| e-ISBN | 9781479904525 |
| DOI | 10.1109/CEC.2013.6557654 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-06-20 |
| Publisher Place | Mexico |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Benchmark testing Coevolution Mutation Testing Search-Based Software Testing Genetic Algorithm |
| Content Type | Text |
| Resource Type | Article |
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