Improving Rule Set Based Software Quality Prediction

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dc.contributor.author Azar, Danielle
dc.contributor.author Bouktif, Salah
dc.contributor.author Sahraoui, Houari
dc.contributor.author Kegl, Balazs
dc.date.accessioned 2016-03-24T10:28:22Z
dc.date.available 2016-03-24T10:28:22Z
dc.date.copyright 2003
dc.date.issued 2016-03-24
dc.identifier.issn 1660-1769 en_US
dc.identifier.uri http://hdl.handle.net/10725/3404
dc.description.abstract The object-oriented (OO) paradigm has now reached maturity. OO software products are becoming more complex which makes their evolution effort and time consuming. In this respect, it has become important to develop tools that allow assessing the stability of OO software (i.e., the ease with which a software item can evolve while preserving its design). In general, predicting the quality of OO software is a complex task. Although many predictive models are proposed in the literature, we remain far from having reliable tools that can be applied to real industrial systems. The main obstacle for building reliable predictive tools for real industrial systems is the lackof representative samples. Unlike other domains where such samples can be drawn from available large repositories of data, in OO software the lack of such repositories makes it hard to generalize, to validate and to reuse existing models. Since universal models do not exist, selecting an appropriate quality model is a difficult, non-trivial decision for a company. In this paper, we propose two general approaches to solve this problem. They consist of combining/adapting a set of existing models. The process is driven by the context of the target company. These approaches are applied to OO software stability prediction. en_US
dc.language.iso en en_US
dc.title Improving Rule Set Based Software Quality Prediction en_US
dc.type Article en_US
dc.description.version Published en_US
dc.title.subtitle A Genetic Algorithm-based Approach en_US
dc.author.school SAS en_US
dc.author.idnumber 198833240 en_US
dc.author.woa N/A en_US
dc.author.department Computer Science and Mathematics en_US
dc.description.embargo N/A en_US
dc.relation.journal Journal of Object and Technology en_US
dc.journal.volume 3 en_US
dc.journal.issue 4 en_US
dc.article.pages 227-241 en_US
dc.identifier.ctation Bouktif, S., Azar, D., Precup, D., Sahraoui, H., & Kegl, B. (2004). Improving rule set based software quality prediction: A genetic algorithm-based approach. Journal of Object Technology, 3(4), 227-241. en_US
dc.author.email danielle.azar@lau.edu.lb
dc.identifier.url http://www.jot.fm/issues/issue_2004_04/article13/
dc.identifier.url http://www.jot.fm/issues/issue_2004_04/article13/

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