A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models
In this work, we present a genetic algorithm to optimize predictive models used to estimate software quality characteristics. Software quality assessment is crucial in the software development field since it helps reduce cost, time and effort. However, software quality characteristics cannot be dire...
Saved in:
| Main Author: | |
|---|---|
| Format: | article |
| Published: |
2010
|
| Online Access: | http://hdl.handle.net/10725/3406 http://dx.doi.org/10.1142/S1469026810002811 http://www.worldscientific.com/doi/abs/10.1142/S1469026810002811?journalCode=ijcia |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513461058273280 |
|---|---|
| author | Azar, Danielle |
| author_facet | Azar, Danielle |
| author_role | author |
| dc.creator.none.fl_str_mv | Azar, Danielle |
| dc.date.none.fl_str_mv | 2010 2016-03-24T11:46:30Z 2016-03-24T11:46:30Z 2016-03-24 |
| dc.identifier.none.fl_str_mv | 1469-0268 http://hdl.handle.net/10725/3406 http://dx.doi.org/10.1142/S1469026810002811 Azar, D. (2010). A genetic algorithm for improving accuracy of software quality predictive models: a search-based software engineering approach. International Journal of Computational Intelligence and Applications, 9(02), 125-136. http://www.worldscientific.com/doi/abs/10.1142/S1469026810002811?journalCode=ijcia |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | International Journal of Computational Intelligence and Applications |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models A Search-Based Software Engineering Approach |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | In this work, we present a genetic algorithm to optimize predictive models used to estimate software quality characteristics. Software quality assessment is crucial in the software development field since it helps reduce cost, time and effort. However, software quality characteristics cannot be directly measured but they can be estimated based on other measurable software attributes (such as coupling, size and complexity). Software quality estimation models establish a relationship between the unmeasurable characteristics and the measurable attributes. However, these models are hard to generalize and reuse on new, unseen software as their accuracy deteriorates significantly. In this paper, we present a genetic algorithm that adapts such models to new data. We give empirical evidence illustrating that our approach out-beats the machine learning algorithm C4.5 and random guess. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | LAURepo_ca744da5acadf8f981aa3bb3e2a04ed5 |
| identifier_str_mv | 1469-0268 Azar, D. (2010). A genetic algorithm for improving accuracy of software quality predictive models: a search-based software engineering approach. International Journal of Computational Intelligence and Applications, 9(02), 125-136. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/3406 |
| publishDate | 2010 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | A Genetic Algorithm for Improving Accuracy of Software Quality Predictive ModelsA Search-Based Software Engineering ApproachAzar, DanielleIn this work, we present a genetic algorithm to optimize predictive models used to estimate software quality characteristics. Software quality assessment is crucial in the software development field since it helps reduce cost, time and effort. However, software quality characteristics cannot be directly measured but they can be estimated based on other measurable software attributes (such as coupling, size and complexity). Software quality estimation models establish a relationship between the unmeasurable characteristics and the measurable attributes. However, these models are hard to generalize and reuse on new, unseen software as their accuracy deteriorates significantly. In this paper, we present a genetic algorithm that adapts such models to new data. We give empirical evidence illustrating that our approach out-beats the machine learning algorithm C4.5 and random guess.PublishedN/A2016-03-24T11:46:30Z2016-03-24T11:46:30Z20102016-03-24Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1469-0268http://hdl.handle.net/10725/3406http://dx.doi.org/10.1142/S1469026810002811Azar, D. (2010). A genetic algorithm for improving accuracy of software quality predictive models: a search-based software engineering approach. International Journal of Computational Intelligence and Applications, 9(02), 125-136.http://www.worldscientific.com/doi/abs/10.1142/S1469026810002811?journalCode=ijciaenInternational Journal of Computational Intelligence and Applicationsinfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/34062017-03-03T14:07:27Z |
| spellingShingle | A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models Azar, Danielle |
| status_str | publishedVersion |
| title | A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models |
| title_full | A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models |
| title_fullStr | A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models |
| title_full_unstemmed | A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models |
| title_short | A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models |
| title_sort | A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models |
| url | http://hdl.handle.net/10725/3406 http://dx.doi.org/10.1142/S1469026810002811 http://www.worldscientific.com/doi/abs/10.1142/S1469026810002811?journalCode=ijcia |