Improving Rule Set Based Software Quality Prediction
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 s...
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| Format: | article |
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2003
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| Online Access: | http://hdl.handle.net/10725/3404 http://www.jot.fm/issues/issue_2004_04/article13/ |
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| _version_ | 1864513461056176128 |
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| author | Azar, Danielle |
| author2 | Bouktif, Salah Sahraoui, Houari Kegl, Balazs |
| author2_role | author author author |
| author_facet | Azar, Danielle Bouktif, Salah Sahraoui, Houari Kegl, Balazs |
| author_role | author |
| dc.creator.none.fl_str_mv | Azar, Danielle Bouktif, Salah Sahraoui, Houari Kegl, Balazs |
| dc.date.none.fl_str_mv | 2003 2016-03-24T10:28:22Z 2016-03-24T10:28:22Z 2016-03-24 |
| dc.identifier.none.fl_str_mv | 1660-1769 http://hdl.handle.net/10725/3404 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. http://www.jot.fm/issues/issue_2004_04/article13/ http://www.jot.fm/issues/issue_2004_04/article13/ |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Journal of Object and Technology |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | Improving Rule Set Based Software Quality Prediction A Genetic Algorithm-based Approach |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | LAURepo_849fd2b5d85b8a4079f75745ef551450 |
| identifier_str_mv | 1660-1769 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. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/3404 |
| publishDate | 2003 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Improving Rule Set Based Software Quality PredictionA Genetic Algorithm-based ApproachAzar, DanielleBouktif, SalahSahraoui, HouariKegl, BalazsThe 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.PublishedN/A2016-03-24T10:28:22Z2016-03-24T10:28:22Z20032016-03-24Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1660-1769http://hdl.handle.net/10725/3404Bouktif, 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.http://www.jot.fm/issues/issue_2004_04/article13/http://www.jot.fm/issues/issue_2004_04/article13/enJournal of Object and Technologyinfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/34042017-03-03T14:17:43Z |
| spellingShingle | Improving Rule Set Based Software Quality Prediction Azar, Danielle |
| status_str | publishedVersion |
| title | Improving Rule Set Based Software Quality Prediction |
| title_full | Improving Rule Set Based Software Quality Prediction |
| title_fullStr | Improving Rule Set Based Software Quality Prediction |
| title_full_unstemmed | Improving Rule Set Based Software Quality Prediction |
| title_short | Improving Rule Set Based Software Quality Prediction |
| title_sort | Improving Rule Set Based Software Quality Prediction |
| url | http://hdl.handle.net/10725/3404 http://www.jot.fm/issues/issue_2004_04/article13/ |