Enhancing Lifecycle Stages and Navigating Implementation Hurdles for AI-Powered Tools and Challenges in Cloud-Native Software Development

Abstract

This chapter examines the transformative impact of artificial intelligence (AI) on software development within a cloud-native context. As the adoption of AI tools has intensified, their integration throughout the Software Development Lifecycle (SDLC) offers both opportunities and challenges. It explores the benefits of AI in areas like requirement analysis, design, implementation, testing, and maintenance, each experiencing advancements in efficiency, accuracy, and scalability. AI aids in automating processes, predicting system issues, optimizing resource use, and enhancing software quality, bringing substantial changes to conventional methods. It emphasizes the specific challenges posed by AI adoption, such as data quality, skill gaps, integration complexity, ethical considerations, and technical scalability. Key sections address the research questions and methodological approaches to understanding the integration barriers. The findings underscore AI's potential to enhance the SDLC but highlight the need for robust strategies.