International Journal of AI and Advanced Computing
Review Article
• Open Access
Advanced Software Modeling and Machine Learning for Robust Software Engineering
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The increasing complexity of modern software systems necessitates robust approaches for ensuring software quality, reliability, and resilience. This comprehensive research paper synthesizes three critical dimensions of software engineering: early bug prediction using machine learning frameworks, advanced fault tolerance modeling techniques for distributed systems, and the systematic analysis of software engineering higher education curricula. Through empirical evaluation, we demonstrate that Random Forest models achieve exceptional performance in software defect prediction with 99.98% accuracy, outperforming baseline models including ANN, CNN, Decision Tree, AdaBoost, and SVM. Our analysis of fault tolerance mechanisms reveals that advanced software modeling techniques—including state machines, Petri nets, and actor-based frameworks—provide systematic approaches for designing resilient distributed systems capable of maintaining operational continuity despite component failures. Additionally, our systematic examination of 207 courses across 10 Dutch universities identifies critical knowledge area correlations and gaps in software engineering education, particularly highlighting the underrepresentation of software engineering economics and configuration management topics. The integration of these three perspectives offers a holistic framework for advancing software engineering practice through predictive analytics, resilient system design, and evidence-based curriculum development. This research contributes actionable insights for practitioners, educators, and researchers seeking to enhance software quality, system reliability, and educational outcomes in the software engineering domain.
Keywords
Software Bug Prediction, Fault Tolerance, Distributed Systems, Machine Learning, Software Engineering Education, Random Forest, DistilBERT, Petri Nets, Actor Model, SWEBOKReferences
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