Academic Journal
Efficient software defect prediction using fuzzy K-member clustering and metaheuristic-driven ensemble feature learning model.
| Τίτλος: | Efficient software defect prediction using fuzzy K-member clustering and metaheuristic-driven ensemble feature learning model. |
|---|---|
| Συγγραφείς: | Javadimoghadam, Shima, Sabagh-Jafari, Seyed Mojtaba, Bardsiri, Amid Khatibi |
| Πηγή: | Cluster Computing; Jun2026, Vol. 29 Issue 3, p1-15, 15p |
| Θεματικοί όροι: | Fuzzy clustering technique, Metaheuristic algorithms, Defect tracking (Computer software development), Machine learning, Data augmentation, Multi-objective optimization, Ensemble learning, Quality assurance |
| Περίληψη: | Software defect prediction that will enable us to create high-quality software at lower costs as well as reduce development costs. However, traditional prediction methods usually do not provide the required precision for effective defect management. Early detection of error-prone modules allows software projects leaders to set priorities in testing, concentrating their attention on the most probable modules. The current paper introduces a hybrid technique consisting of K-member Fuzzy Clustering and a Cost-sensitive Multi-objective Metaheuristic-driven Ensemble Feature Learning (also called KFC-CMMEFL), which is a new method of error detection in software. The suggested technique starts with the data preparation phase, during which K-member fuzzy clustering and oversampling techniques are applied in order to handle the missing data problem and evaluate redundant features. Next, the offline optimization phase is deployed where the NSGA-II multi-objective algorithm adjusts the. model's hyperparameters, including classifier-specific features, ensemble weights, final classification threshold, and other controllable parameters. After being optimized with 10-fold cross-validation, the optimized KFC-CMMEFL model then changes over to an online phase which leads to even better defect classification. Through the use of balanced fuzzy clustering and a cost-sensitive metaheuristic-optimized ensemble feature learning model, our approach is able to achieve a better trade-off between true-positives and false-positives, thus, the accuracy of defect predictions climbs. The experiments we conducted on 10 different datasets from tera-PROMISE showed that KFC-CMMEFL outperforms the referent methods in software defect prediction field. [ABSTRACT FROM AUTHOR] |
| Copyright of Cluster Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10586-026-05961-w Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 191518562 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.4189453125 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Efficient software defect prediction using fuzzy K-member clustering and metaheuristic-driven ensemble feature learning model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Javadimoghadam%2C+Shima%22">Javadimoghadam, Shima</searchLink><br /><searchLink fieldCode="AR" term="%22Sabagh-Jafari%2C+Seyed+Mojtaba%22">Sabagh-Jafari, Seyed Mojtaba</searchLink><br /><searchLink fieldCode="AR" term="%22Bardsiri%2C+Amid+Khatibi%22">Bardsiri, Amid Khatibi</searchLink> – Name: TitleSource Label: Source Group: Src Data: Cluster Computing; Jun2026, Vol. 29 Issue 3, p1-15, 15p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Fuzzy+clustering+technique%22">Fuzzy clustering technique</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Defect+tracking+%28Computer+software+development%29%22">Defect tracking (Computer software development)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+assurance%22">Quality assurance</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Software defect prediction that will enable us to create high-quality software at lower costs as well as reduce development costs. However, traditional prediction methods usually do not provide the required precision for effective defect management. Early detection of error-prone modules allows software projects leaders to set priorities in testing, concentrating their attention on the most probable modules. The current paper introduces a hybrid technique consisting of K-member Fuzzy Clustering and a Cost-sensitive Multi-objective Metaheuristic-driven Ensemble Feature Learning (also called KFC-CMMEFL), which is a new method of error detection in software. The suggested technique starts with the data preparation phase, during which K-member fuzzy clustering and oversampling techniques are applied in order to handle the missing data problem and evaluate redundant features. Next, the offline optimization phase is deployed where the NSGA-II multi-objective algorithm adjusts the. model's hyperparameters, including classifier-specific features, ensemble weights, final classification threshold, and other controllable parameters. After being optimized with 10-fold cross-validation, the optimized KFC-CMMEFL model then changes over to an online phase which leads to even better defect classification. Through the use of balanced fuzzy clustering and a cost-sensitive metaheuristic-optimized ensemble feature learning model, our approach is able to achieve a better trade-off between true-positives and false-positives, thus, the accuracy of defect predictions climbs. The experiments we conducted on 10 different datasets from tera-PROMISE showed that KFC-CMMEFL outperforms the referent methods in software defect prediction field. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Cluster Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=191518562 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10586-026-05961-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: Fuzzy clustering technique Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Defect tracking (Computer software development) Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Data augmentation Type: general – SubjectFull: Multi-objective optimization Type: general – SubjectFull: Ensemble learning Type: general – SubjectFull: Quality assurance Type: general Titles: – TitleFull: Efficient software defect prediction using fuzzy K-member clustering and metaheuristic-driven ensemble feature learning model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Javadimoghadam, Shima – PersonEntity: Name: NameFull: Sabagh-Jafari, Seyed Mojtaba – PersonEntity: Name: NameFull: Bardsiri, Amid Khatibi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13867857 Numbering: – Type: volume Value: 29 – Type: issue Value: 3 Titles: – TitleFull: Cluster Computing Type: main |
| ResultId | 1 |