A genetic algorithm for corrective retesting. (c1995)

Includes bibliographical references.

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Fakih, Khaled A. (author)
التنسيق: masterThesis
منشور في: 1995
الموضوعات:
الوصول للمادة أونلاين:http://hdl.handle.net/10725/323
https://doi.org/10.26756/th.1995.12
الوسوم: إضافة وسم
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author Fakih, Khaled A.
author_facet Fakih, Khaled A.
author_role author
dc.creator.none.fl_str_mv Fakih, Khaled A.
dc.date.none.fl_str_mv 1995
1995-05
2011-04-05T12:04:21Z
2011-04-05T12:04:21Z
2011-04-05
dc.identifier.none.fl_str_mv http://hdl.handle.net/10725/323
https://doi.org/10.26756/th.1995.12
dc.language.none.fl_str_mv en
dc.publisher.none.fl_str_mv Lebanese American University
dc.rights.*.fl_str_mv info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Algorithms
Combinatorial optimization
Testing -- Data processing
dc.title.none.fl_str_mv A genetic algorithm for corrective retesting. (c1995)
dc.type.none.fl_str_mv Thesis
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/masterThesis
description Includes bibliographical references.
eu_rights_str_mv openAccess
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id LAURepo_567b38605d0cc2e5e6b3e8b905e7487f
language_invalid_str_mv en
network_acronym_str LAURepo
network_name_str Lebanese American University repository
oai_identifier_str oai:laur.lau.edu.lb:10725/323
publishDate 1995
publisher.none.fl_str_mv Lebanese American University
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
spelling A genetic algorithm for corrective retesting. (c1995)Fakih, Khaled A.AlgorithmsCombinatorial optimizationTesting -- Data processingIncludes bibliographical references.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.1 bound copy: 1 v. (various pagings); ill., tables available at RNL.Lebanese American University2011-04-05T12:04:21Z2011-04-05T12:04:21Z19952011-04-051995-05Thesisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://hdl.handle.net/10725/323https://doi.org/10.26756/th.1995.12eninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/3232020-05-18T14:53:44Z
spellingShingle A genetic algorithm for corrective retesting. (c1995)
Fakih, Khaled A.
Algorithms
Combinatorial optimization
Testing -- Data processing
status_str publishedVersion
title A genetic algorithm for corrective retesting. (c1995)
title_full A genetic algorithm for corrective retesting. (c1995)
title_fullStr A genetic algorithm for corrective retesting. (c1995)
title_full_unstemmed A genetic algorithm for corrective retesting. (c1995)
title_short A genetic algorithm for corrective retesting. (c1995)
title_sort A genetic algorithm for corrective retesting. (c1995)
topic Algorithms
Combinatorial optimization
Testing -- Data processing
url http://hdl.handle.net/10725/323
https://doi.org/10.26756/th.1995.12