Systematic Review of AI-Augmented Refactoring and Code Validation Tools in Aviation Technology Platforms

As aviation technology platforms become increasingly complex and software-intensive, ensuring code quality, maintainability, and compliance with stringent safety standards has become a critical priority. This systematic review examines the integration of artificial intelligence into software refactoring and code validation processes within the aviation domain. The study explores foundational concepts, evaluates real-world applications, and synthesizes academic and industry findings to assess the current landscape of AI-augmented development tools. It distinguishes between traditional and AI-driven approaches, highlighting how machine learning and predictive analytics enhance static and dynamic code analysis, formal verification, and regression testing. Through case studies from leading aerospace institutions and a review of performance metrics, the paper identifies key strengths, such as efficiency gains and automated compliance support, alongside critical limitations including explainability, generalizability, and domain-specific data scarcity. It concludes with practical and theoretical recommendations, advocating for interdisciplinary collaboration and the development of certifiable, explainable AI systems tailored to safety-critical aviation environments. This review contributes to a growing body of knowledge informing future tool development, academic inquiry, and strategic adoption within the aerospace software engineering ecosystem.