Introducing dependency, exception and encapsulation metrics for object-oriented defect prediction: Complementarity with CK/MOOD metrics through empirical machine learning and statistical validation
A set of ten novel object-oriented metrics designed to enrich existing datasets and enhance prediction performance are proposed, demonstrating that the proposed metrics capture complementary aspects of software quality not fully represented by traditional metrics, leading to more accurate and robust defect prediction models.
It is suggested that process-oriented metrics, particularly those related to code testing and development history, capture defect patterns more effectively per feature than static code structure metrics, offering practical guidance for software quality assurance.
Ioana-Gabriela Chelaru, G. Czibula, Zuzsanna Oneţ-Marian et al.· Acta Universitatis Sapientia...· 0 citations
Objectives: The primary objective of this research is to estimate maintenance effort using complexity metrics and to demonstrate that the two selected metrics, CWICC and ICWCC, provide more accurate maintenance effort predictions compared to existing software complexity metrics. This will be validated through the application of various statistical techniques. Method: Two cognitive complexity metrics—Cognitive Weighted Inheritance Class Complexity (CWICC) and Interface-based Cognitive Weighted Class Complexity (ICWCC) were identified and empirically analyzed. Statistical techniques, including simple and multiple regression analysis, were applied to a collected dataset. The results were further compared with existing object-oriented metrics. Findings: The analysis demonstrates that cognitive complexity metrics can effectively predict software maintenance effort. The results also highlight differences in performance when compared to traditional object-oriented metrics, providing insights into their relative effectiveness. Novelty: This study introduces and validates two cognitive complexity metrics, CWICC and ICWCC to predict software maintenance effort. The novelty of this research work lies in establishing that the proposed metrics enable more accurate prediction of maintenance effort through closer alignment between predicted and actual maintenance time; for instance, ICWCC (160.54) and CWICC (159.09) closely approximate the observed value (162.08), outperforming WCC (139.32) and AWCC (154.29), thereby confirming their effectiveness as reliable predictors of software maintenance effort.
Keywords: Maintenance, Effort, Cognitive Complexity Metrics, Maintenance Time, Correlation, Regression
K. Maheswaran, A. Shreenath· Indian Journal of Science an...· 0 citations
The software defect prediction problem is difficult because of high-dimensional feature spaces, redundancy in software metrics and severe class imbalance. There has been a large body of work proposed on machine learning methods, however, few works have focused on the stability and generalizability of selected features across heterogeneous datasets.
A comprehensive multi-method feature selection framework that combines filter (Mutual Information, Chi-Square), wrapper (Recursive Feature Elimination) and embedded (Random Forest) methods and finally a consensus-based ranking mechanism for finding stable and dataset-independent software metrics is proposed. The framework is tested on several PROMISE datasets (KC1, KC2, PC1, JM1), representing various software systems and distributions of metrics.
Experimental results show that the size and complexity related metrics such as Lines of Code (LOC), Halstead measures and cyclomatic complexity invariably sit on top of all data sets and selection approaches, resulting in high feature stability. The results of the classification based on Logis-tic Regression, however, have found a significant difference between the measure of precision (0.58-0.65) and the measure of re-call (0.23-0.31), which means that the classification is not so effective in detecting the minority of defective in-stances.
The results show that, in imbalanced conditions, using robust feature selection is not enough for early defect prediction. The study highlights the importance of embedding imbalance-aware learning techniques (cost-sensitive learning, synthetic sampling, and ensemble models). The proposed framework shows good feature stability and has a high F1-score in multiple datasets with a value up to 0.42, which means it is effective at cross-dataset defect prediction.
Papiya Mukherjee, Mamta Dahiya· Journal of Intelligent Decis...· 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
Software defect prediction is essential for maintaining code quality in critical domains, yet it remains challenging due to feature redundancy and class imbalance. This study proposes an optimized Pi–Sigma Neural Network (PSNN) framework leveraging Correlation-Based Feature Selection (CBFS) and Min-Max normalization. Utilizing the NASA PROMISE CM1 dataset, a 5-fold stratified cross-validation pipeline was implemented to ensure statistical robustness and prevent data leakage. Experimental results on the CM1 dataset show the refined PSNN achieves high performance (99.80% accuracy on CM1) after aggressive feature reduction to 3–5 features and a precision of 1.000. To address class imbalance, the model achieved a Matthews Correlation Coefficient (MCC) of 0.988 and a G-Mean of 0.990. Comparative analysis shows that the developed PSNN-FS, despite its simplicity, achieves strong performance competitive with more complex architectures on the CM1 dataset.
Barka Piyinkir Ndahi, O. Abisoye, O. Ojerinde et al.· Bulletin of the National Res...· 0 citations
These findings validate cognitive theory for explainable, actionable, and interpretable safety-critical defect prediction, laying empirical groundwork to evaluate analogous issues in LLM-generated code through the behavioral study of AI.
Carlos Andrés Ramírez Cataño, Makoto Itoh· International Conference on...· 0 citations
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