Artificial Intelligence as a Catalyst for Innovation in Software Engineering

TL;DR

This paper investigates how Artificial Intelligence enhances software engineering by automating tasks and improving Agile practices, supported by literature review and a survey of professionals, revealing AI's role in fostering innovation and efficiency.

Contribution

It provides an empirical analysis of AI's impact on software engineering, highlighting new capabilities and integration strategies for Agile development.

Findings

AI automates requirement management, coding, and testing tasks

AI integration improves Agile practice efficiency

AI fosters innovation in software development processes

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.