Artificial Intelligence as a Catalyst for Innovation in Software Engineering
- URL: http://arxiv.org/abs/2603.10994v1
- Date: Wed, 11 Mar 2026 17:20:30 GMT
- Title: Artificial Intelligence as a Catalyst for Innovation in Software Engineering
- Abstract summary: This article explores how AI serves as a powerful catalyst for enhancing agility and fostering innovation.<n>The integration of AI (specifically through Machine Learning (ML) and Natural Language Processing (NLP))facilitates the automation of tedious tasks.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Preparing Students for AI-Driven Agile Development: A Project-Based AI Engineering Curriculum [1.7791007833336614]
This paper presents a project-based AI Engineering curriculum designed to prepare students for AI-driven agile development.<n>We contribute (1) the curriculum concept and guiding principles, (2) a case study of interdisciplinary, AI-enabled agile student projects, and (3) early evidence from a mixed-methods evaluation.
arXiv Detail & Related papers (2026-03-10T12:44:23Z) - Generative AI and the Transformation of Software Development Practices [0.0]
Generative AI is reshaping how software is designed, written, and maintained.<n>This paper examines how AI-assisted techniques are changing software engineering practice.
arXiv Detail & Related papers (2025-10-12T22:02:10Z) - Multi-agent Embodied AI: Advances and Future Directions [46.23631919950584]
Embodied artificial intelligence (Embodied AI) plays a pivotal role in the application of advanced technologies in the intelligent era.<n>This paper reviews the current state of research, analyzes key contributions, and identifies challenges and future directions.
arXiv Detail & Related papers (2025-05-08T10:13:53Z) - Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
arXiv Detail & Related papers (2025-05-03T13:55:38Z) - Challenges and Paths Towards AI for Software Engineering [55.95365538122656]
We discuss progress in AI for software engineering in threefold manner.<n>First, we provide a structured taxonomy of concrete tasks in AI for software engineering.<n>Second, we outline several key bottlenecks that limit current approaches.
arXiv Detail & Related papers (2025-03-28T17:17:57Z) - AI's Impact on Traditional Software Development [0.0]
The application of artificial intelligence (AI) has brought key shifts in conventional tactical software development.<n>This paper examines the technical aspect of integrating AI into prior traditional software development life cycle methodologies.
arXiv Detail & Related papers (2025-02-05T14:58:09Z) - Exploring the intersection of Generative AI and Software Development [0.0]
The synergy between generative AI and Software Engineering emerges as a transformative frontier.
This whitepaper delves into the unexplored realm, elucidating how generative AI techniques can revolutionize software development.
It serves as a guide for stakeholders, urging discussions and experiments in the application of generative AI in Software Engineering.
arXiv Detail & Related papers (2023-12-21T19:23:23Z) - Prompted Software Engineering in the Era of AI Models [1.450405446885067]
This paper introduces prompted software engineering (PSE), which integrates prompt engineering to build effective prompts for language-based AI models.
PSE enables the use of AI models in software development to produce high-quality software with fewer resources, automating tedious tasks and allowing developers to focus on more innovative aspects.
arXiv Detail & Related papers (2023-09-07T20:40:04Z) - Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring [81.06807079998117]
We propose a novel approach to enable Automated Machine Learning (AutoML) for Non-Intrusive Appliance Load Monitoring (NIALM)<n>NIALM offers a cost-effective alternative to smart meters for measuring the energy consumption of electric devices and appliances.
arXiv Detail & Related papers (2022-03-06T10:12:56Z) - Empowered and Embedded: Ethics and Agile Processes [60.63670249088117]
We argue that ethical considerations need to be embedded into the (agile) software development process.
We put emphasis on the possibility to implement ethical deliberations in already existing and well established agile software development processes.
arXiv Detail & Related papers (2021-07-15T11:14:03Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Technology Readiness Levels for AI & ML [79.22051549519989]
Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results.
We propose a proven systems engineering approach for machine learning development and deployment.
arXiv Detail & Related papers (2020-06-21T17:14:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.