GENERATIVE AI IN THE SOFTWARE DEVELOPMENT LIFECYCLE (SDLC): OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS

Generative Artificial Intelligence (AI) is fundamentally transforming the Software Development Lifecycle (SDLC) by enabling automation in code generation, enhancing software quality assurance, and streamlining development workflows. A detailed examination of the integration of Generative AI across all major phases of the SDLC, including requirements engineering, architectural and system design, implementation, testing, deployment, and post-deployment maintenance. A methodical assessment of state-of-the-art AI-assisted development tools—such as GitHub Copilot, ChatGPT, and Tabnine—is undertaken to evaluate their practical utility and impact. Furthermore, the study investigates the technical benefits, operational challenges, and ethical implications arising from the adoption of AI within the SDLC. The paper concludes with proposed directions for future research aimed at advancing secure, interpretable, and ethically-aligned AI-driven methodologies in software engineering. Keywords: Generative AI, Software Development Lifecycle (SDLC), AI-driven Automation, Machine Learning in Software Engineering, AI-powered Software Testing, Ethical AI in Software Development