A genetic algorithm for corrective retesting. (c1995)

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dc.contributor.author Fakih, Khaled A.
dc.date.accessioned 2011-04-05T12:04:21Z
dc.date.available 2011-04-05T12:04:21Z
dc.date.copyright 1995 en_US
dc.date.issued 2011-04-05
dc.date.submitted 1995-05
dc.identifier.uri http://hdl.handle.net/10725/323
dc.description Includes bibliographical references. en_US
dc.description.abstract The optimal retesting problem is that of determining the minimum number of test cases needed for revalidating modified software in the maintenance phase. We present a genetic algorithm (GA) for solving this problem, based on the program's flow graph and an integer programming problem formulation. The algorithm deviates from classical GAs in that it incorporates some design choices to guarantee a final feasible solution and to improve the efficiency of the genetic search. These choices include elitist ranking, random feasibilization, penalization, and a hybridized hill-climbing procedure. The main advantage of this algorithm is that it does not suffer from exponential explosion for large program sizes. Further, the experimental results show that it finds an optimal number of retests faster than other known methods. en_US
dc.language.iso en en_US
dc.subject Algorithms en_US
dc.subject Combinatorial optimization en_US
dc.subject Testing -- Data processing en_US
dc.title A genetic algorithm for corrective retesting. (c1995) en_US
dc.type Thesis en_US
dc.term.submitted Spring en_US
dc.author.degree MS in Computer Science en_US
dc.author.school Arts and Sciences en_US
dc.author.commembers Dr. George Nasr
dc.author.commembers Dr. Haidar Harmanani
dc.author.woa RA en_US
dc.description.physdesc 1 bound copy: 1 v. (various pagings); ill., tables available at RNL. en_US
dc.author.division Computer Science en_US
dc.author.advisor Dr. Nashat Mansour
dc.identifier.doi https://doi.org/10.26756/th.1995.12 en_US
dc.publisher.institution Lebanese American University en_US

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