Simulated Annealing and Genetic Algorithms for Optimal Regression Testing
The optimal regression testing problem is one of determining the minimum number of test cases needed for revalidating modified software in the maintenance phase. We present two natural optimization algorithms, namely, a simulated annealing and a genetic algorithm, for solving this problem. The algor...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | article |
| منشور في: |
1999
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| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/2968 http://dx.doi.org/10.1002/(SICI)1096-908X(199901/02)11:13.0.CO;2-M http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1096-908X(199901/02)11:1%3C19::AID-SMR182%3E3.0.CO;2-M/abstract |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513459741261824 |
|---|---|
| author | Mansour, Nashat |
| author2 | El-Fakih, Khaled |
| author2_role | author |
| author_facet | Mansour, Nashat El-Fakih, Khaled |
| author_role | author |
| dc.creator.none.fl_str_mv | Mansour, Nashat El-Fakih, Khaled |
| dc.date.none.fl_str_mv | 1999 2016-01-27T11:06:40Z 2016-01-27T11:06:40Z 2016-01-27 |
| dc.identifier.none.fl_str_mv | 1040-550X http://hdl.handle.net/10725/2968 http://dx.doi.org/10.1002/(SICI)1096-908X(199901/02)11:13.0.CO;2-M Mansour, N., & El-Fakih, K. (1999). Simulated annealing and genetic algorithms for optimal regression testing. Journal of Software Maintenance, 11(1), 19-34. http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1096-908X(199901/02)11:1%3C19::AID-SMR182%3E3.0.CO;2-M/abstract |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Journal of software maintenance: Research and practice |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | Simulated Annealing and Genetic Algorithms for Optimal Regression Testing |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | The optimal regression testing problem is one of determining the minimum number of test cases needed for revalidating modified software in the maintenance phase. We present two natural optimization algorithms, namely, a simulated annealing and a genetic algorithm, for solving this problem. The algorithms are based on an integer programming problem formulation and the program’s control flow graph. The main advantage of these algorithms, in comparison with exact algorithms, is that they do not suffer from an exponential explosion for realistic program sizes. The experimental results, which include a comparison with previous algorithms, show that the simulated annealing and genetic algorithms find the optimal or near-optimal number of retests within a reasonable time. Copyright 1999 John Wiley & Sons, Ltd. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | LAURepo_48950fe3acc05f5e285c0c5bb034db87 |
| identifier_str_mv | 1040-550X Mansour, N., & El-Fakih, K. (1999). Simulated annealing and genetic algorithms for optimal regression testing. Journal of Software Maintenance, 11(1), 19-34. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/2968 |
| publishDate | 1999 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Simulated Annealing and Genetic Algorithms for Optimal Regression TestingMansour, NashatEl-Fakih, KhaledThe optimal regression testing problem is one of determining the minimum number of test cases needed for revalidating modified software in the maintenance phase. We present two natural optimization algorithms, namely, a simulated annealing and a genetic algorithm, for solving this problem. The algorithms are based on an integer programming problem formulation and the program’s control flow graph. The main advantage of these algorithms, in comparison with exact algorithms, is that they do not suffer from an exponential explosion for realistic program sizes. The experimental results, which include a comparison with previous algorithms, show that the simulated annealing and genetic algorithms find the optimal or near-optimal number of retests within a reasonable time. Copyright 1999 John Wiley & Sons, Ltd.PublishedN/A2016-01-27T11:06:40Z2016-01-27T11:06:40Z19992016-01-27Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1040-550Xhttp://hdl.handle.net/10725/2968http://dx.doi.org/10.1002/(SICI)1096-908X(199901/02)11:13.0.CO;2-MMansour, N., & El-Fakih, K. (1999). Simulated annealing and genetic algorithms for optimal regression testing. Journal of Software Maintenance, 11(1), 19-34.http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1096-908X(199901/02)11:1%3C19::AID-SMR182%3E3.0.CO;2-M/abstractenJournal of software maintenance: Research and practiceinfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/29682016-08-04T07:17:59Z |
| spellingShingle | Simulated Annealing and Genetic Algorithms for Optimal Regression Testing Mansour, Nashat |
| status_str | publishedVersion |
| title | Simulated Annealing and Genetic Algorithms for Optimal Regression Testing |
| title_full | Simulated Annealing and Genetic Algorithms for Optimal Regression Testing |
| title_fullStr | Simulated Annealing and Genetic Algorithms for Optimal Regression Testing |
| title_full_unstemmed | Simulated Annealing and Genetic Algorithms for Optimal Regression Testing |
| title_short | Simulated Annealing and Genetic Algorithms for Optimal Regression Testing |
| title_sort | Simulated Annealing and Genetic Algorithms for Optimal Regression Testing |
| url | http://hdl.handle.net/10725/2968 http://dx.doi.org/10.1002/(SICI)1096-908X(199901/02)11:13.0.CO;2-M http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1096-908X(199901/02)11:1%3C19::AID-SMR182%3E3.0.CO;2-M/abstract |