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Constructing Covering Arrays using Parallel Computing and Grid Computing

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Constructing Covering Arrays using Parallel Computing and Grid Computing

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dc.contributor.advisor Hernández García, Vicente es_ES
dc.contributor.advisor Torres Jimenez, Jose es_ES
dc.contributor.author Avila George, Himer es_ES
dc.date.accessioned 2012-09-10T06:14:36Z
dc.date.available 2012-09-10T06:14:36Z
dc.date.created 2012-07-30T08:00:00Z es_ES
dc.date.issued 2012-09-10T06:14:33Z es_ES
dc.identifier.uri http://hdl.handle.net/10251/17027
dc.description.abstract A good strategy to test a software component involves the generation of the whole set of cases that participate in its operation. While testing only individual values may not be enough, exhaustive testing of all possible combinations is not always feasible. An alternative technique to accomplish this goal is called combinato- rial testing. Combinatorial testing is a method that can reduce cost and increase the effectiveness of software testing for many applications. It is based on con- structing functional test-suites of economical size, which provide coverage of the most prevalent configurations. Covering arrays are combinatorial objects, that have been applied to do functional tests of software components. The use of cov- ering arrays allows to test all the interactions, of a given size, among the input parameters using the minimum number of test cases. For software testing, the fundamental problem is finding a covering array with the minimum possible number of rows, thus reducing the number of tests, the cost, and the time expended on the software testing process. Because of the importance of the construction of (near) optimal covering arrays, much research has been carried out in developing effective methods for constructing them. There are several reported methods for constructing these combinatorial models, among them are: (1) algebraic methods, recursive methods, (3) greedy methods, and (4) metaheuristics methods. Metaheuristic methods, particularly through the application of simulated anneal- ing has provided the most accurate results in several instances to date. Simulated annealing algorithm is a general-purpose stochastic optimization method that has proved to be an effective tool for approximating globally optimal solutions to many optimization problems. However, one of the major drawbacks of the simulated an- nealing is the time it requires to obtain good solutions. In this thesis, we propose the development of an improved simulated annealing algorithm es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.rights Reserva de todos los derechos es_ES
dc.source Riunet es_ES
dc.subject Covering arrays es_ES
dc.subject Parallel computing es_ES
dc.subject Grid computing es_ES
dc.subject Combinatorial optimization es_ES
dc.subject Simulated annealing es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.title Constructing Covering Arrays using Parallel Computing and Grid Computing
dc.type Tesis doctoral es_ES
dc.identifier.doi 10.4995/Thesis/10251/17027 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Avila George, H. (2012). Constructing Covering Arrays using Parallel Computing and Grid Computing [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17027 es_ES
dc.description.accrualMethod Palancia es_ES
dc.type.version info:eu-repo/semantics/acceptedVersion es_ES
dc.relation.tesis 3917 es_ES


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