Agile Software Development in AI-Driven Applications: Challenges and Strategies

Abstract

Agile software development, known for its flexibility and iterative approach, has been significantly impacted by the advancements in Artificial Intelligence (AI). AI integration enhances efficiency, speed, and quality throughout the software development lifecycle. AI-powered tools can automate repetitive tasks like code generation, testing, and bug fixing, freeing up time for innovation. Predictive analytics, driven by AI, empowers Agile teams to forecast potential issues, estimate project timelines accurately, and make well-informed decisions. AI also improves software quality through anomaly detection, bug identification, and comprehensive testing, ensuring more extensive test coverage and accuracy. Furthermore, AI enables the development of personalized software solutions by analyzing user behavior and preferences, leading to user-centric applications and enhanced user satisfaction. AI-driven analytics provide actionable insights into team performance, code quality, and user feedback, facilitating continuous improvement and adaptation. AI algorithms can also assist in resource allocation by analyzing project requirements and team capabilities, optimizing work distribution and increasing productivity. The integration of Generative AI into Agile methodologies streamlines development workflows and fosters innovation. However, it's important to note that there are challenges and considerations when introducing AI to Agile, such as the lack of effectiveness in existing agile applications. Despite these challenges, AI integration into Agile practices redefines software development, creating more robust, intelligent, and usercentric applications