Understanding Everything as Code: A Taxonomy and Conceptual Model

• URL: http://arxiv.org/abs/2507.05100v1
• Date: Mon, 07 Jul 2025 15:21:02 GMT
• Title: Understanding Everything as Code: A Taxonomy and Conceptual Model
• Authors: Haoran Wei, Nazim Madhavji, John Steinbacher,
• Abstract summary: Everything as Code (EaC) is an emerging paradigm aiming to codify all aspects of modern software systems.<n>Despite its growing popularity, comprehensive industry standards and peer-reviewed research clarifying its scope and guiding its adoption remain scarce.<n>This study systematically analyzes existing knowledge and perceptions of EaC, clarifies its scope and boundaries, and provides structured guidance for researchers and practitioners.
• Score: 8.82214863392834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: Everything as Code (EaC) is an emerging paradigm aiming to codify all aspects of modern software systems. Despite its growing popularity, comprehensive industry standards and peer-reviewed research clarifying its scope and guiding its adoption remain scarce. Aims: This study systematically analyzes existing knowledge and perceptions of EaC, clarifies its scope and boundaries, and provides structured guidance for researchers and practitioners. Method: We conducted a large-scale multivocal literature review (MLR), synthesizing academic and grey literature sources. Findings were analyzed quantitatively and thematically. Based on this analysis, we developed a taxonomy and conceptual model of EaC, validated through collaboration with industry experts. Results: The resulting taxonomy comprises 25 distinct EaC practices organized into six layers based on industry awareness and functional roles. The conceptual model illustrates focus areas, overlaps, and interactions among these EaC practices within the software delivery lifecycle. Additionally, practical code examples demonstrating the implementation of these practices were developed in collaboration with industry experts. Conclusions: This work addresses the current scarcity of academic discourse on EaC by providing the first comprehensive taxonomy and conceptual model. These contributions enhance conceptual clarity, offer actionable guidance to practitioners, and lay the groundwork for future research in this emerging domain.

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