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dc.contributor.authorDel Águila Cano, Isabel María
dc.contributor.authorSagrado Martínez, José del
dc.date.accessioned2016-11-02T07:41:06Z
dc.date.available2016-11-02T07:41:06Z
dc.date.issued2011-03
dc.identifier.citationhttp://dx.doi.org/10.1142/S0218194011005219es_ES
dc.identifier.issn1793-6403
dc.identifier.urihttp://hdl.handle.net/10835/4465
dc.description.abstractRequirement engineering is a key issue in the development of a software project. Like any other development activity it is not without risks. This work is about the empirical study of risks of requirements by applying machine learning techniques, specifically Bayesian networks classifiers. We have defined several models to predict the risk level for a given requirement using three dataset that collect metrics taken from the requirement specifications of different projects. The classification accuracy of the Bayesian models obtained is evaluated and compared using several classification performance measures. The results of the experiments show that the Bayesians networks allow obtaining valid predictors. Specifically, a tree augmented network structure shows a competitive experimental performance in all datasets. Besides, the relations established between the variables collected to determine the level of risk in a requirement, match with those set by requirement engineers. We show that Bayesian networks are valid tools for the automation of risks assessment in requirement engineering.es_ES
dc.language.isoenes_ES
dc.publisherWordl Scientifices_ES
dc.titleRequirement Risk Level Forecast Using Bayesian Networks Classifierses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttp://www.worldscientific.com/doi/abs/10.1142/S0218194011005219es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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