Artificial Intelligence as a Catalyst for Innovation in Software Engineering
2026-03-11 • Software Engineering
Software EngineeringArtificial Intelligence
AI summaryⓘ
The authors looked into how Artificial Intelligence (AI) can help software teams work better and faster when making changes to software. They studied current research and surveyed professionals to see how AI tools are being used. Their findings show that AI methods like Machine Learning and Natural Language Processing can handle boring repetitive tasks such as managing requirements and writing code. This makes Agile development smoother and helps keep software quality high even with time pressure. The authors suggest that AI will be important for future software work to stay efficient and innovative.
Agile software developmentArtificial IntelligenceMachine LearningNatural Language ProcessingSoftware requirementsCode generationSoftware testingAutomationSoftware quality
Authors
Carlos Alberto Fernández-y-Fernández, Jorge R. Aguilar-Cisneros
Abstract
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.