AI-Powered Software Development Life Cycle: From Requirements to Maintenance

The integration of artificial intelligence into software development represents a paradigmatic shift with profound implications for productivity, quality, and efficiency in modern software engineering practices. This viewpoint paper aims to examine the comprehensive application of generative AI technologies across all Software Development Life Cycle (SDLC) phases, analyze their transformative potential, and provide strategic insights for successful implementation. Through systematic analysis of recent empirical studies and practical implementations, we conducted a comprehensive review of AI applications spanning requirements analysis, system design, coding, testing, deployment, and maintenance phases. AI demonstrates significant capabilities in automating code generation, enhancing testing strategies, optimizing deployment processes, and enabling proactive maintenance approaches. Key applications include natural language processing for requirements refinement, intelligent architecture recommendations, comprehensive test suite generation, and predictive bug detection. Research Implications: Successful AI integration requires gradual implementation strategies, robust quality assurance mechanisms, and maintaining human oversight for critical decisions. Organizations must balance AI automation with human expertise, addressing ethical considerations and security concerns while fostering continuous learning approaches that align AI capabilities with organizational standards and domain-specific requirements.