An Evolutionary Approach to Test SELECT SQL Statements using Mutation Analysis
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 s...
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| Veröffentlicht in: | Revista IEEE América Latina 2017-06, Vol.15 (6), p.1128-1136 |
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| creator | Moncao, A.C Camilo Junior, C.G. Queiroz, L.T. Rodrigues, C.L. Leitao Junior, P.S. Vincenzi, A.M. Araujo, A.A. Dantas, A. de Souza, J.T. |
| description | 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. |
| doi_str_mv | 10.1109/TLA.2017.7932701 |
| format | Article |
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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.</description><identifier>ISSN: 1548-0992</identifier><identifier>EISSN: 1548-0992</identifier><identifier>DOI: 10.1109/TLA.2017.7932701</identifier><language>eng</language><publisher>Los Alamitos: IEEE</publisher><subject>Evolutionary algorithms ; Fault detection ; Genetic Algorithm ; Genetic algorithms ; IEEE transactions ; In vitro ; Mutation ; Mutation Analysis ; Optimization ; Query languages ; Search-Based Software Testing ; Software ; Software testing ; SQL Statements</subject><ispartof>Revista IEEE América Latina, 2017-06, Vol.15 (6), p.1128-1136</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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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.</description><subject>Evolutionary algorithms</subject><subject>Fault detection</subject><subject>Genetic Algorithm</subject><subject>Genetic algorithms</subject><subject>IEEE transactions</subject><subject>In vitro</subject><subject>Mutation</subject><subject>Mutation Analysis</subject><subject>Optimization</subject><subject>Query languages</subject><subject>Search-Based Software Testing</subject><subject>Software</subject><subject>Software testing</subject><subject>SQL Statements</subject><issn>1548-0992</issn><issn>1548-0992</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkM1Lw0AQxRdRsFbvgpcFz4k7u9mPHEOJHxAVaTwv2-1GU9qkZjdC_3u3tIqnGWbeG978ELoGkgKQ_K6uipQSkKnMGZUETtAEeKYSkuf09F9_ji68XxHClFBsgl6KDpff_XoMbd-ZYYeL7Xbojf3Eoce18wHPy6qc1Xj-VuF5MMFtXBc8Hn3bfeDnMU6iERedWe986y_RWWPW3l0d6xS935f17DGpXh-eZkWVWJBZSBohuZVuQTjlTDG5tHQhQQluDVuIRjS2EVQq1xgpiTBOCiYgB0ayTBnKl2yKbg93Y9ivMcbUq34cYgivISeUSg6gooocVHbovR9co7dDu4lfaiB6T01HanpPTR-pRcvNwdI65_7kv9sfNA1mvw</recordid><startdate>20170601</startdate><enddate>20170601</enddate><creator>Moncao, A.C</creator><creator>Camilo Junior, C.G.</creator><creator>Queiroz, L.T.</creator><creator>Rodrigues, C.L.</creator><creator>Leitao Junior, P.S.</creator><creator>Vincenzi, A.M.</creator><creator>Araujo, A.A.</creator><creator>Dantas, A.</creator><creator>de Souza, J.T.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Evolutionary algorithms Fault detection Genetic Algorithm Genetic algorithms IEEE transactions In vitro Mutation Mutation Analysis Optimization Query languages Search-Based Software Testing Software Software testing SQL Statements |
| title | An Evolutionary Approach to Test SELECT SQL Statements using Mutation Analysis |
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