The AI-Native Software Development Lifecycle: A Theoretical and Practical New Methodology
- URL: http://arxiv.org/abs/2408.03416v3
- Date: Tue, 27 Aug 2024 19:10:23 GMT
- Title: The AI-Native Software Development Lifecycle: A Theoretical and Practical New Methodology
- Authors: Cory Hymel,
- Abstract summary: This white paper proposes the emergence of a fully AI-native SDLC.
We introduce the V-Bounce model, an adaptation of the traditional V-model that incorporates AI from end to end.
This model redefines the role of humans from primary implementers to primarily validators and verifiers with AI acting as an implementation engine.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As AI continues to advance and impact every phase of the software development lifecycle (SDLC), a need for a new way of building software will emerge. By analyzing the factors that influence the current state of the SDLC and how those will change with AI we propose a new model of development. This white paper proposes the emergence of a fully AI-native SDLC, where AI is integrated seamlessly into every phase of development, from planning to deployment. We introduce the V-Bounce model, an adaptation of the traditional V-model that incorporates AI from end to end. The V-Bounce model leverages AI to dramatically reduce time spent in implementation phases, shifting emphasis towards requirements gathering, architecture design, and continuous validation. This model redefines the role of humans from primary implementers to primarily validators and verifiers with AI acting as an implementation engine.
Related papers
- Generative Diffusion-based Contract Design for Efficient AI Twins Migration in Vehicular Embodied AI Networks [55.15079732226397]
Embodied AI is a rapidly advancing field that bridges the gap between cyberspace and physical space.
In VEANET, embodied AI twins act as in-vehicle AI assistants to perform diverse tasks supporting autonomous driving.
arXiv Detail & Related papers (2024-10-02T02:20:42Z) - tl;dr: Chill, y'all: AI Will Not Devour SE [5.77648992672856]
Social media provide a steady diet of dire warnings that artificial intelligence (AI) will make software engineering (SE) irrelevant or obsolete.
To the contrary, the engineering discipline of software is rich and robust.
Machine learning, large language models (LLMs) and generative AI will offer new opportunities to extend the models and methods of SE.
arXiv Detail & Related papers (2024-09-01T16:16:33Z) - A call for embodied AI [1.7544885995294304]
We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence.
By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures.
This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development.
arXiv Detail & Related papers (2024-02-06T09:11:20Z) - Exploration with Principles for Diverse AI Supervision [88.61687950039662]
Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI.
While this generative AI approach has produced impressive results, it heavily leans on human supervision.
This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation.
We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
arXiv Detail & Related papers (2023-10-13T07:03:39Z) - AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs.
This paper explores the innovative concept of harnessing these AI-generated images as new data sources.
In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
arXiv Detail & Related papers (2023-10-03T06:55:19Z) - 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) - Data-Driven and SE-assisted AI Model Signal-Awareness Enhancement and
Introspection [61.571331422347875]
We propose a data-driven approach to enhance models' signal-awareness.
We combine the SE concept of code complexity with the AI technique of curriculum learning.
We achieve up to 4.8x improvement in model signal awareness.
arXiv Detail & Related papers (2021-11-10T17:58:18Z) - Time for AI (Ethics) Maturity Model Is Now [15.870654219935972]
This paper argues that AI software is still software and needs to be approached from the software development perspective.
We wish to discuss whether the focus should be on AI ethics or, more broadly, the quality of an AI system.
arXiv Detail & Related papers (2021-01-29T17:37:44Z) - Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI
Inference Engines in Autonomous Vehicles [1.688204090869186]
This paper proposes a novel framework for developing AI Inference Engines for autonomous driving applications based on deep learning modules.
We introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm.
The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles.
arXiv Detail & Related papers (2020-09-23T09:23:29Z) - Developing and Operating Artificial Intelligence Models in Trustworthy
Autonomous Systems [8.27310353898034]
This work-in-progress paper aims to close the gap between the development and operation of AI-based AS.
We propose a novel, holistic DevOps approach to put it into practice.
arXiv Detail & Related papers (2020-03-11T17:52:30Z)
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.