Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
Overall, the findings indicate that integrating principled feature selection with a boosting-based stacking ensemble can improve software fault prediction performance while providing greater transparency for software quality management.
Abstract
The increasing complexity of contemporary software systems makes accurate and interpretable software fault prediction a challenging task. Although ensemble learning techniques have demonstrated promising predictive performance, existing approaches continue to face challenges related to feature selection, cross-project consistency, and model interpretability. This study proposes a Hybrid Boosted Stacking Ensemble Framework (HBSEF) for software fault prediction that integrates hybrid feature selection, ensemble learning, statistical validation, and SHAP-based interpretability. The proposed framework incorporates data preprocessing, class-imbalance handling using Random Oversampling, and a hybrid feature selection strategy integrating filter, embedded, and wrapper methods to identify software metrics with consistent predictive importance across multiple software projects. The core predictive model employs CatBoost, XGBoost, and LightGBM as base learners, with XGBoost serving as the meta-learner in the stacking ensemble. The framework is evaluated on an unseen test set using standard classification metrics, while paired t-testing is employed to assess the statistical significance of performance differences. Experimental results identify LOC, CBO, WMC, RFC, LCOM, and MAX_CC as the most influential software metrics. The proposed framework demonstrates statistically significant improvements over the baseline ensemble models, as validated through paired t-testing (t = 4.53, p = 0.0004), indicating the effectiveness of the proposed approach in enhancing software fault prediction performance. SHAP-based explanations provide both global and local interpretations, revealing the contribution of individual software metrics and base-model predictions to fault classification. Overall, the findings indicate that integrating principled feature selection with a boosting-based stacking ensemble can improve software fault prediction performance while providing greater transparency for software quality management.
Software defect prediction (SDP) often faces challenges related to heterogeneous software metrics, classifier dependency, and severe class imbalance, which may limit the robustness and generalization of feature selection strategies. This study proposes an adaptive feature selection approach to construct structured and discriminative feature subsets that remain effective across diverse datasets and learning models. The proposed method first measures the relationship between each software metric and the defect label using absolute determination power and then applies an adaptive retention rule to iteratively retain features with stronger discriminative contribution. The evaluation was conducted on multiple public defect datasets using several classical machine learning classifiers. Unlike approaches optimized for specific classifiers, the proposed strategy emphasizes cross-classifier robustness and imbalance-aware evaluation through defect recall and Matthews correlation coefficient. Experimental results show that the proposed method achieves a competitive average MCC of 0.257 and a defect recall of 0.446 compared with baseline approaches, although the statistical tests do not indicate significant superiority. Therefore, the proposed method should be interpreted as a comparable and stable alternative for feature selection under imbalanced SDP conditions. Stability and statistical analyses further indicate that the proposed method maintains comparable performance across dataset-classifier combinations. In addition, feature compactness analysis shows that the performance gains are attributable to efficient, interpretable feature subsets, highlighting the importance of robustness-oriented feature selection in SDP.
Aris Puji Widodo, Prajanto Wahyu Adi, Y. Ashari et al.· Discover Artificial Intellig...· 0 citations
An intelligent machine learning-based bug prediction framework that uses SMOTE for dataset balancing and feature selection to identify the most relevant software metrics and uses advanced ensemble learning techniques, such as CatBoost, LightGBM, and the Stacking Ensemble model, to improve prediction accuracy.
Bhukya Yashaswini· International Journal of Eng...· 0 citations
This study presents an explainable machine learning framework for early prediction of student academic performance using a stacking-based ensemble approach. The proposed model integrates Random Forest (RF), XGBoost, and Support Vector Machine (SVM) as base learners, with Logistic Regression (LR) used as a meta-learner. A dataset of 2,392 student records, containing demographic and behavioral features, was used for training and evaluation. Data preprocessing included encoding and normalization, followed by model optimization using cross-validation and grid search. The experimental results demonstrated that the proposed stacking model achieves strong predictive performance, with an accuracy of 95.82% and an Area Under the ROC Curve (AUC) close to 1.0 on the primary dataset, while maintaining strong generalization across external datasets. Model interpretability is enhanced using SHapley Additive exPlanations (SHAP) analysis, which identifies Grade Point Average (GPA), attendance, and study time as the most influential factors. In addition, the framework is implemented as a web-based system for real-time prediction and decision support. The results demonstrate that the proposed approach is a practical solution for educational data analytics.
Tole Bi Yermek, Amanzhol Yelemessov, B. Yergesh et al.· Engineering, Technology &...· 0 citations
An Explainable Artificial Intelligence (XAI) driven framework for developing composite, transparent software quality metrics that integrate predictive accuracy with multi-level interpretability and provides a scalable and extensible foundation for transparent AI-driven software engineering tools.
Abdulaziz Attaallah, Khalil Al Sulbi· IEEE Access· 0 citations
Student attrition poses a significant challenge in higher education. It negatively impacts academic performance, reputation, and resource management. This paper presents an XAI-based stacking ensemble for early prediction of student attrition with EDM methods.We used a publicly available dataset on Kaggle, which contained information about 4,424 undergraduate students and their personal characteristics, academic status and socioeconomic background.Class imbalance is dealt with using a cost-sensitive learning strategy with the parameter of scale_pos_weight combined with stratified train-test splitting. The proposed stacking ensemble framework used the Random Forest and XGBoost as base models and Logistic Regression as a meta-learner. The selected threshold of classification for early prediction was 0.20. The proposed model performance is assessed with several performance metrics, including Accuracy, Precision, Recall, F1-score, ROC-AUC, PR-AUC, as well as calibration analysis.Experimental results showed that the proposed stacking ensemble outperformed individual base models and offered the best prediction accuracy with a high Recall value in early dropout prediction.The developed model interpretability by SHAP and LIME showed that student performance and financial situation played a significant role in predicting the risk of attrition at early stages of study. The predicted probabilities are divided into the categories of Low, Medium and High-Risk using percentiles, to facilitate interventions for the students at risk. In this context, we offered an interpretable and applicable artificial intelligence-driven early warning system in higher education.
Rupali Ambalal Jadhav, Rupal Parekh· International journal of com...· 0 citations
: This study proposes a Stacked Regressor (SR) model for software defect count prediction. The approach combines four heterogeneous base learners—Support Vector Regression (SVR), k -Nearest Neighbors (KNN), Random Forest Regressor (RFR), and Extra Trees Regressor (ETR)—whose predictions are aggregated through a meta-regressor. Two SR variants are implemented, differing only in the choice of the meta-regressor: SVR or RFR. The models are evaluated on 10 publicly available software projects comprising 34 versions under within-version and within-project settings. Experimental results show that the proposed SR approach consistently outperforms individual base learners in terms of MAE and MRE, while remaining competitive in RMSE and Pred(0.3). Statistical analysis further supports the robustness of the observed improvements. These findings indicate that stacking heterogeneous regression models can enhance the stability and generalization of software defect count prediction.
Rim El Jammal, Elissa El Khoury, Leonardo Daou et al.· International Conference on...· 0 citations
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