Ensuring software quality is critical for the reliability, maintainability, and usability of modern software systems. Traditional software quality assessment techniques often rely on manual reviews, static analysis, or classical machine learning models that offer limited interpretability. This research proposes an Explainable Machine Learning (XML)–based framework to assess software quality by integrating code metrics, defect datasets, and advanced interpretability methods such as SHAP, LIME, and permutation importance. The study evaluates multiple ML models—Random Forest, Gradient Boosting, XGBoost, and Neural Networks—to predict software quality attributes including reliability, maintainability, and defect proneness. Explainability techniques are applied to interpret model decisions, identify key quality indicators, and provide insights useful for developers, testers, and project managers. Experimental results demonstrate that explainable ML improves both predictive performance and decision transparency, making it suitable for practical software engineering environments. This research highlights how combining ML with explainability techniques enhances trust, interpretability, and actionable insights in software quality assessment.
Nandhini Ravi· International Journal of Mac...· 0 citations
The rapid growth of unstructured and heterogeneous data in modern information systems has created a need for intelligent methods to extract, organize, and utilize knowledge effectively. AI-based Knowledge Graphs (KGs) address this challenge by representing entities and their relationships in a semantically rich graph structure, enabling advanced reasoning and decision support. By integrating machine learning, natural language processing, and deep learning, KGs automate entity extraction, relationship identification, and knowledge inference, improving decision-making across domains such as healthcare, finance, e-commerce, and governance. This paper presents a framework combining data preprocessing, ontology development, graph embedding, and inference techniques. Experimental results show that AI-driven knowledge graphs significantly enhance decision accuracy, reduce ambiguity, and improve interpretability, achieving up to 85–92% higher decision efficiency compared to traditional methods. Future research focuses on scalability, explainability, and integration with emerging technologies like IoT and edge computing.
Venkatesh Iyer, Nandhini Ravi· International Journal of Art...· 0 citations