Mostrar el registro sencillo del ítem

dc.contributor.authorFerrer, Javier
dc.contributor.authorChicano, Francisco 
dc.contributor.authorAlba-Torres, Enrique 
dc.date.accessioned2014-10-03T07:05:01Z
dc.date.available2014-10-03T07:05:01Z
dc.date.issued2014-10-03
dc.identifier.otherDOI: 10.1002/spe.1135
dc.identifier.urihttp://hdl.handle.net/10630/8165
dc.descriptionSoftware: Practice & Experience, 42(11):1331-1362es_ES
dc.description.abstractAutomatic test data generation is a very popular domain in the field of search-based software engineering. Traditionally, the main goal has been to maximize coverage. However, other objectives can be defined, such as the oracle cost, which is the cost of executing the entire test suite and the cost of checking the system behavior. Indeed, in very large software systems, the cost spent to test the system can be an issue, and then it makes sense by considering two conflicting objectives: maximizing the coverage and minimizing the oracle cost. This is what we did in this paper. We mainly compared two approaches to deal with the multi-objective test data generation problem: a direct multi-objective approach and a combination of a mono-objective algorithm together with multi-objective test case selection optimization. Concretely, in this work, we used four state-of-the-art multi-objective algorithms and two mono-objective evolutionary algorithms followed by a multi-objective test case selection based on Pareto efficiency. The experimental analysis compares these techniques on two different benchmarks. The first one is composed of 800 Java programs created through a program generator. The second benchmark is composed of 13 real programs extracted from the literature. In the direct multi-objective approach, the results indicate that the oracle cost can be properly optimized; however, the full branch coverage of the system poses a great challenge. Regarding the mono-objective algorithms, although they need a second phase of test case selection for reducing the oracle cost, they are very effective in maximizing the branch coverage.es_ES
dc.description.sponsorshipSpanish Ministry of Science and Innovation and FEDER under contract TIN2008-06491-C04-01 (the M project). Andalusian Government under contract P07-TIC-03044 (DIRICOM project).es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectComputación evolutivaes_ES
dc.subject.otherMulti-objective test data generationes_ES
dc.subject.otherBranch coveragees_ES
dc.subject.otherOracle costes_ES
dc.subject.otherEvolutionary testinges_ES
dc.subject.otherEvolutionary algorithmses_ES
dc.subject.otherSearch-based software engineeringes_ES
dc.titleEvolutionary algorithms for the multi-objective test data generation problemes_ES
dc.typeinfo:eu-repo/semantics/preprintes_ES
dc.centroE.T.S.I. Informáticaes_ES


Ficheros en el ítem

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem