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This paper investigates the role of AI, specifically ML and NLP, in enhancing agility and innovation within software engineering. It combines a literature review with a survey of software engineering professionals to assess the perception, adoption, and impact of AI-driven tools. The study finds that AI facilitates the automation of tedious tasks such as requirement management, code generation, and testing, thereby optimizing Agile practices and introducing new capabilities for quality, speed, and innovation.
AI's integration into software engineering isn't just streamlining existing Agile processes; it's unlocking entirely new capabilities for maintaining quality and speed under pressure.
The rapid evolution and inherent complexity of modern software requirements demand highly flexible and responsive development methodologies. While Agile frameworks have become the industry standard for prioritizing iteration, collaboration, and adaptability, software development teams continue to face persistent challenges in managing constantly evolving requirements and maintaining product quality under tight deadlines. This article explores the intersection of Artificial Intelligence (AI) and Software Engineering (SE), to analyze how AI serves as a powerful catalyst for enhancing agility and fostering innovation. The research combines a comprehensive review of existing literature with an empirical study, utilizing a survey directed at Software Engineering professionals to assess the perception, adoption, and impact of AI-driven tools. Key findings reveal that the integration of AI (specifically through Machine Learning (ML) and Natural Language Processing (NLP) )facilitates the automation of tedious tasks, from requirement management to code generation and testing . This paper demonstrates that AI not only optimizes current Agile practices but also introduces new capabilities essential for sustaining quality, speed, and innovation in the future landscape of software development.
