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The AI-Native Software Development Lifecycle: A Theoretical and Practical New Methodology

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arxiv 2408.03416 v3 pith:IDJIGQMF submitted 2024-08-06 cs.SE

classification cs.SE
keywords developmentmodelsdlcsoftwareai-nativeeveryimplementationlifecycle
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

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  1. A Grey Literature Review of AI-Native Applications

    cs.SE 2025-09 conditional novelty 5.0 of 10

    A systematic grey literature review identifies the defining characteristics, quality attributes, and technology stacks of AI-native applications, and proposes a dual-layered engineering blueprint.

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