Software Quality Assessment Using Explainable Machine Learning

Explainable AI-Driven Metrics for Transparent Software Quality Prediction

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.

Predicting Software Maintainability Using AI Models

Software maintainability is a critical quality attribute that directly impacts the long-term cost, reliability, and evolution of software systems. Predicting maintainability early in the development lifecycle enables developers and managers to make informed design decisions, allocate resources efficiently, and reduce technical debt. This paper investigates the use of artificial intelligence (AI) models for predicting software maintainability based on code metrics, historical project data, and architectural characteristics. We explore supervised learning techniques, including regression models, decision trees, and neural networks, as well as ensemble and hybrid approaches, to estimate maintainability scores and identify key factors influencing maintainability. Experiments on open-source and industrial datasets demonstrate the effectiveness of AI-based predictions in improving software quality assessment, providing actionable insights, and supporting proactive maintenance strategies. The study highlights the potential of AI-driven methods to enhance software engineering practices and reduce maintenance effort.

ML-Enhanced Code Refactoring Recommendations for Improving Software Maintainability

Results show that ML-enhanced recommendations outperform traditional methods in accuracy, relevance, and impact on maintainability metrics, and highlight the potential of integrating ML into modern development practices to support developers in producing cleaner, more maintainable software systems.

INTELLIGENT BUG PREDICTION SYSTEM

The intelligent bug prediction system addresses the main problem of identifying the defect-prone software modules in the early stages of the software development life cycle. The conventional testing methodologies are time-consuming and costly and do not provide an effective way to prioritise high-risk modules. This problem is important for improving software reliability, reducing maintenance cost and improving the overall quality of software systems. The current bug prediction techniques mainly rely on traditional machine learning models such as Random Forests, Support Vector Machines (SVMs), and Neural Networks, but they face challenges such as imbalanced data, limited feature sets, low interpretability, and binary predictions that do not provide meaningful guidance for testing prioritisation.In our approach, we propose an intelligent machine learning-based bug prediction framework that uses SMOTE for dataset balancing and feature selection to identify the most relevant software metrics. We also use advanced ensemble learning techniques, such as CatBoost, LightGBM, and the Stacking Ensemble model, to improve prediction accuracy. Methodology: Analysed software metrics from NASA MDP evaluated model performance using Accuracy, Precision, Recall, F1-score, ROC-AUC, and Confusion Matrix; and applied SHAP (SHapley Additive Explanations) to provide transparent and interpretable predictions. A Risk Scoring Mechanism categorises software modules as Stable, High, Medium, or Low risk, and Bug Fix Recommendations help developers more effectively fix predicted defects.The proposed system is expected to produce accurate, explainable, and risk-aware bug predictions. This enables developers to prioritise testing efforts, optimise resource allocation, cut debugging costs, and deliver more dependable, secure, and high-quality software.

An Explainable Feature Selection and Stacking Ensemble Framework for Software Fault Prediction

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.

Automated software debugging and bug prediction through the use of machine learning and deep learning