The Future of Software Engineering in an AI-Driven World
- URL: http://arxiv.org/abs/2406.07737v1
- Date: Tue, 11 Jun 2024 21:46:19 GMT
- Title: The Future of Software Engineering in an AI-Driven World
- Authors: Valerio Terragni, Partha Roop, Kelly Blincoe,
- Abstract summary: In the next five years, we will likely see an increasing symbiotic partnership between human developers and AI.
We present our vision of the future of software development in an AI-Driven world and explore the key challenges that our research community should address to realize this vision.
- Score: 4.915744683251151
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A paradigm shift is underway in Software Engineering, with AI systems such as LLMs gaining increasing importance for improving software development productivity. This trend is anticipated to persist. In the next five years, we will likely see an increasing symbiotic partnership between human developers and AI. The Software Engineering research community cannot afford to overlook this trend; we must address the key research challenges posed by the integration of AI into the software development process. In this paper, we present our vision of the future of software development in an AI-Driven world and explore the key challenges that our research community should address to realize this vision.
Related papers
- Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap [30.996760992473064]
Software Engineering 3.0 (SE 3.0) is an AI-native approach characterized by intent-first, conversation-oriented development.
We outline the key components of the SE 3.0 technology stack, which includes Teammate.next for adaptive and personalized AI partnership.
This paper lays the foundation for future discussions on the role of AI in the next era of software engineering.
arXiv Detail & Related papers (2024-10-08T15:04:07Z) - Rethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers [30.996760992473064]
We propose a paradigm shift towards goal-driven AI-powered pair programmers that collaborate with human developers.
We envision AI pair programmers that are goal-driven, human partners, SE-aware, and self-learning.
arXiv Detail & Related papers (2024-04-16T02:10:20Z) - Bridging Gaps, Building Futures: Advancing Software Developer Diversity and Inclusion Through Future-Oriented Research [50.545824691484796]
We present insights from SE researchers and practitioners on challenges and solutions regarding diversity and inclusion in SE.
We share potential utopian and dystopian visions of the future and provide future research directions and implications for academia and industry.
arXiv Detail & Related papers (2024-04-10T16:18:11Z) - 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) - Embedded Software Development with Digital Twins: Specific Requirements
for Small and Medium-Sized Enterprises [55.57032418885258]
Digital twins have the potential for cost-effective software development and maintenance strategies.
We interviewed SMEs about their current development processes.
First results show that real-time requirements prevent, to date, a Software-in-the-Loop development approach.
arXiv Detail & Related papers (2023-09-17T08:56:36Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - AI Maintenance: A Robustness Perspective [91.28724422822003]
We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance.
We propose an AI model inspection framework to detect and mitigate robustness risks.
Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
arXiv Detail & Related papers (2023-01-08T15:02:38Z) - Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering.
In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
arXiv Detail & Related papers (2022-03-06T10:12:56Z) - Artificial Intelligence for the Metaverse: A Survey [66.57225253532748]
We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse.
We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse.
Several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds.
arXiv Detail & Related papers (2022-02-15T03:34:56Z) - Building an AI-ready RSE Workforce [0.0]
Machine learning and deep learning are being applied in every aspect of the research software development lifecycles.
We discuss our views on today's challenges and opportunities that AI has presented on research software development and engineers.
arXiv Detail & Related papers (2021-11-09T02:36:24Z)
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.