A Comprehensive Framework for Intelligent, Scalable, and Performance-Optimized Software Development

Abstract

Integrating Artificial Intelligence (AI) into the Software Development Life Cycle (SDLC) has become necessary to enhance efficiency, scalability, and performance in modern software systems. Instead of incorporating the AI functionality into their SDLC, traditional SDLC models typically add-on the AI software functionality after they have integrated AI functionality into their application or software process. Because of this, developers undergo inefficiencies in their development workflows, experience performance bottlenecks during testing, and experience challenges of incorporating AI to improve an application’s performance through optimization. This paper proposes a new AI-Optimized Software Development Life Cycle (AI-SDLC), which is a holistic and comprehensive framework that encases the embedded AI capabilities and optimization strategies throughout the SDLC process during every stage of the system development, so that requirements-gathering, development, testing, and maintenance are hybrid software processes and not dictated by AI vs. traditional software development processes. AI-SDLC presents new development roles, such as AI Integration Specialist, Code Optimizer, and UX Optimization Specialist, which helps developers work across disciplines and increases collaborative interaction between traditional developers and AI engineers. AI-SDLC also utilizes an AI-driven automated hybrid software process in areas such as requirement elicitation, design/architecture validation, testing, deployment monitoring, and scalability to produce robust high-performance systems in all areas of practicing software development life cycle work. The discourse includes a rich case study based on a Smart Logistics Management System to demonstrate practical implementation of the AI-SDLC and how it facilitates improvement in system efficiency and improved user experience. Additionally, the discussion also highlights the possibilities of AI-SDLC practical implementation in other industrial domain areas such as e-Commerce, finance, aviation and enterprise solution based projects with practical considerations for implementation. In conclusion, the discussion provides findings that support AI-SDLC as a structured and intelligence-driven approach to Software Development Life Cycle implementation that addresses the weaknesses of traditional software design and development frameworks.