Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap
- URL: http://arxiv.org/abs/2410.06107v1
- Date: Tue, 8 Oct 2024 15:04:07 GMT
- Title: Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap
- Authors: Ahmed E. Hassan, Gustavo A. Oliva, Dayi Lin, Boyuan Chen, Zhen Ming, Jiang,
- Abstract summary: 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.
- Score: 30.996760992473064
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The rise of AI-assisted software engineering (SE 2.0), powered by Foundation Models (FMs) and FM-powered copilots, has shown promise in improving developer productivity. However, it has also exposed inherent limitations, such as cognitive overload on developers and inefficiencies. We propose a shift towards Software Engineering 3.0 (SE 3.0), an AI-native approach characterized by intent-first, conversation-oriented development between human developers and AI teammates. SE 3.0 envisions AI systems evolving beyond task-driven copilots into intelligent collaborators, capable of deeply understanding and reasoning about software engineering principles and intents. We outline the key components of the SE 3.0 technology stack, which includes Teammate.next for adaptive and personalized AI partnership, IDE.next for intent-first conversation-oriented development, Compiler.next for multi-objective code synthesis, and Runtime.next for SLA-aware execution with edge-computing support. Our vision addresses the inefficiencies and cognitive strain of SE 2.0 by fostering a symbiotic relationship between human developers and AI, maximizing their complementary strengths. We also present a roadmap of challenges that must be overcome to realize our vision of SE 3.0. This paper lays the foundation for future discussions on the role of AI in the next era of software engineering.
Related papers
- The Future of Software Engineering in an AI-Driven World [4.915744683251151]
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.
arXiv Detail & Related papers (2024-06-11T21:46:19Z) - From Today's Code to Tomorrow's Symphony: The AI Transformation of Developer's Routine by 2030 [3.437372707846067]
We provide a comparative analysis between the current state of AI-assisted programming in 2024 and our projections for 2030.
We envision HyperAssistant, an augmented AI tool that offers comprehensive support to 2030 developers.
arXiv Detail & Related papers (2024-05-21T12:37:36Z) - The Ethics of Advanced AI Assistants [53.89899371095332]
This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants.
We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user.
We consider the deployment of advanced assistants at a societal scale, focusing on cooperation, equity and access, misinformation, economic impact, the environment and how best to evaluate advanced AI assistants.
arXiv Detail & Related papers (2024-04-24T23:18:46Z) - 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) - Trustworthy and Synergistic Artificial Intelligence for Software
Engineering: Vision and Roadmaps [8.521736336052635]
This Future of Software Engineering (FoSE) paper navigates through several focal points.
The paper paints a vision for the potential leaps achievable if AI4SE's key challenges are surmounted.
The ultimate aspiration is to position AI4SE as a linchpin in redefining the horizons of software engineering.
arXiv Detail & Related papers (2023-09-08T05:53:24Z) - 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) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork [54.309495231017344]
We argue that AI systems should be trained in a human-centered manner, directly optimized for team performance.
We study this proposal for a specific type of human-AI teaming, where the human overseer chooses to either accept the AI recommendation or solve the task themselves.
Our experiments with linear and non-linear models on real-world, high-stakes datasets show that the most accuracy AI may not lead to highest team performance.
arXiv Detail & Related papers (2020-04-27T19:06:28Z)
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.