Enhancing Software Engineering Paradigms through the Integration of Artificial Intelligence and Machine Learning for Adaptive Development Optimization Intelligent Code Generation and Predictive Quality Assurance in Complex Software Lifecycle Environments
Keywords:
Artificial Intelligence, Machine Learning, Software Engineering, Code Generation, Quality Assurance, Adaptive Development, DevOps, Deep Learning, Predictive Maintenance, AutomationAbstract
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into software engineering paradigms is redefining how systems are developed, optimized, and maintained. By leveraging intelligent code generation, adaptive development workflows, and predictive quality assurance techniques, modern software engineering is evolving to meet the demands of complex and dynamic environments. This paper presents a focused examination of how AI and ML tools contribute to adaptive development optimization, automate coding tasks, and enhance quality prediction mechanisms throughout the software lifecycle. We review the current state of research, discuss prominent methodologies, present empirical findings from key studies, and propose an integrated AI-ML framework for sustainable software engineering.
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Copyright (c) 2025 Andrei Alexandrescu (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
