Enhanced Software Defect Prediction: NSGA-II-based K-member Clustering
and Ensemble Learning
- Shima Javadimoghadam,
- Seyed Mojtaba Sabagh-Jafari
, - Amid Khatibi Bardsiri
Shima Javadimoghadam
Islamic Azad University Kerman Branch
Author ProfileSeyed Mojtaba Sabagh-Jafari
![](https://d197for5662m48.cloudfront.net/assets/icons/omniauth/orcid-ca37582cd91a671ed5cec57770e0e96c01b4b79b8b5b75d8598fc1ddac18c974.svg)
Rafsanjan University of Vali Asr
Corresponding Author:mojtaba.sabbagh@vru.ac.ir
Author ProfileAbstract
Predicting software defects can improve software assurance reliability
and reduce development costs. Traditional predictions usually lack
precision. The early detection of fault-prone modules helps software
project managers allocate developers' limited time for testing the
defect-prone modules more intensively. Traditional methods for
predicting software defects often lack precision, highlighting the need
for enhanced techniques. The proposed solution introduces an ensemble
learning algorithm based on clustering for software defect prediction,
aiming to handle imbalanced datasets and manage redundant features. The
method incorporates clustering and oversampling for data preparation,
followed by cost-sensitive feature selection and ensemble learning for
improved classification. By employing clustering to group similar data
and addressing imbalanced datasets through K-member fuzzy clustering
algorithms, the strategy enhances the accuracy of defect prediction. The
optimization stage involves selecting the best classifier, features for
each cluster, and their hyper-parameters using the NSGA-II algorithm.
Experiments conducted on real-world datasets demonstrate better
performance of the suggested approach in contrast to other established
approaches in the software defect detection literature.