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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.03416 |
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| _version_ | 1866916372711211008 |
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| author | Hymel, Cory |
| author_facet | Hymel, Cory |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03416 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The AI-Native Software Development Lifecycle: A Theoretical and Practical New Methodology Hymel, Cory Software Engineering 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. |
| title | The AI-Native Software Development Lifecycle: A Theoretical and Practical New Methodology |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2408.03416 |