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Dynamic generation of test cases with metaheuristics

By:

La Battaglia, Juan Pablo

Contributor(s):

Lanzarini, Laura Cristina

Material type: Article

ArticlePublication details: ref_localidad@NULL : Red de Universidades Nacionales con Carreras de Informática (RedUNCI), 2009Description: 1 archivo (124,6 KB)Subject(s):

Online resources:

Summary: The resolution of optimization problems is of great interest nowadays and has encouraged the development of various information technology methods to attempt solving them. There are several prob- lems related to Software Engineering that can be solved by using this approach. In this paper, a new alternative based on the combination of population metaheuristics with a Tabu List to solve the problem of test cases generation when testing software is presented. This problem is of great importance for the development of software with a high compu- tational cost and which is generally hard to solve. The performance of the solution proposed has been tested on a set of varying complexity programs. The results obtained show that the method proposed allows obtaining a reduced test data set in a suitable timeframe and with a greater coverage than conventional methods such as Random Method or Tabu Search.

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Item type	Current library	Call number	Status	Date due	Barcode
Capítulo de libro	Biblioteca Fac.Informática	A0423 (Browse shelf(Opens below))	Available		DIF-A0423

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Formato de archivo: PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)

The resolution of optimization problems is of great interest nowadays and has encouraged the development of various information technology methods to attempt solving them. There are several prob- lems related to Software Engineering that can be solved by using this approach. In this paper, a new alternative based on the combination of population metaheuristics with a Tabu List to solve the problem of test cases generation when testing software is presented. This problem is of great importance for the development of software with a high compu- tational cost and which is generally hard to solve. The performance of the solution proposed has been tested on a set of varying complexity programs. The results obtained show that the method proposed allows obtaining a reduced test data set in a suitable timeframe and with a greater coverage than conventional methods such as Random Method or Tabu Search.

Congreso Argentino de Ciencias de la Computación (25to. : 2009 oct : Jujuy), pp. 1267-1275

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