Bibliographic Details
| Title: |
Efficient software defect prediction using fuzzy K-member clustering and metaheuristic-driven ensemble feature learning model. |
| Authors: |
Javadimoghadam, Shima, Sabagh-Jafari, Seyed Mojtaba, Bardsiri, Amid Khatibi |
| Source: |
Cluster Computing; Jun2026, Vol. 29 Issue 3, p1-15, 15p |
| Subject Terms: |
Fuzzy clustering technique, Metaheuristic algorithms, Defect tracking (Computer software development), Machine learning, Data augmentation, Multi-objective optimization, Ensemble learning, Quality assurance |
| Abstract: |
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] |
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| Database: |
Complementary Index |