# Artificial Intelligence as a Catalyst for Innovation in Software Engineering

arXiv: https://arxiv.org/abs/2603.10994 · HTML: https://mlsanity.com/paper/2603.10994/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 11 Mar 2026.

## Builds on (most cited first, 8 of 8)

- [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022)
- [Navigating the Complexity of Generative AI Adoption in Software Engineering](https://mlsanity.com/paper/2307.06081/) (2023)
- [Exploring and Characterizing Large Language Models For Embedded System Development and Debugging](https://mlsanity.com/paper/2307.03817/) (2023)
- [From Copilot to Pilot: Towards AI Supported Software Development](https://mlsanity.com/paper/2303.04142/) (2023)
- [ChatGPT as a tool for User Story Quality Evaluation: Trustworthy Out of the Box?](https://mlsanity.com/paper/2306.12132/) (2023)
- [Autonomous Agents in Software Development: A Vision Paper](https://mlsanity.com/paper/2311.18440/) (2023)
- [A Comprehensive Survey of AI-Driven Advancements and Techniques in Automated Program Repair and Code Generation](https://mlsanity.com/paper/2411.07586/) (2024)
- [Artificial Intelligence and its Role in Near Future](https://mlsanity.com/paper/1804.01396/) (2018)

## Abstract (arXiv)

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.