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• W2619901905 abstract "This paper proposes combining the mutation testing technique with evolutionary computing to improve the test data applied to SELECT instructions. From a heuristic perspective, this approach uses Genetic Algorithms to optimize tuples selection from an original database. In other words, generating a smaller amount of tuples able to detect faults in the instructions. Mutants are analyzed to evaluate each set of data tests selected during the evolutionary process. Once the appropriate reduced database was found, it can be used whenever the SQL statement test is necessary. The experimental results indicate that the metaheuristics outperform random methods and reach, in average, 80.3% of the optimal value." @default.
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• W2619901905 date "2017-06-01" @default.
• W2619901905 modified "2023-09-26" @default.
• W2619901905 title "An Evolutionary Approach to Test SELECT SQL Statements using Mutation Analysis" @default.
• W2619901905 doi "https://doi.org/10.1109/tla.2017.7932701" @default.
• W2619901905 hasPublicationYear "2017" @default.
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