Paper detail

An Empirical Study of Policy-as-Code Adoption in Open-Source Software Projects

\textbf{Context:} Policy-as-Code (PaC) has become a foundational approach for embedding governance, compliance, and security requirements directly into software systems. While organizations increasingly adopt PaC tools, the software engineering community lacks an empirical understanding of how these tools are used in real-world development practices. \textbf{Objective:} This paper aims to bridge this gap by conducting the first large-scale study of PaC usage in open-source software. Our goal is to characterize how PaC tools are adopted, what purposes they serve, and what governance activities they support across diverse software ecosystems. \textbf{Method:} We analyzed 399 GitHub repositories using nine widely adopted PaC tools. Our mixed-methods approach combines quantitative analysis of tool usage and project characteristics with a qualitative investigation of policy files. We further employ a Large Language Model (LLM)--assisted classification pipeline, refined through expert validation, to derive a taxonomy of PaC usage consisting of 5 categories and 15 sub-categories. \textbf{Results:} Our study reveals substantial diversity in PaC adoption. PaC tools are frequently used in early-stage projects and are heavily oriented toward governance, configuration control, and documentation. We also observe emerging PaC usage in MLOps pipelines and strong co-usage patterns, such as between OPA and Gatekeeper. Our taxonomy highlights recurring governance intents. \textbf{Conclusion:} Our findings offer actionable insights for practitioners and tool developers. They highlight concrete usage patterns, emphasize actual PaC usage, and motivate opportunities for improving tool interoperability. This study lays the empirical foundation for future research on PaC practices and their role in ensuring trustworthy, compliant software systems.

preprint2026arXivOpen access
0citations
0reviews
0saves
Nocode
Nodataset
0institutions

Next steps

Decide what to do with this paper

Use like or dislike for the fast social read. The more specific scholarly feedback stays available below when needed.

Log in to curate

Reading frame

Keep the important context close to the paper

Keep the important signals around this paper in one place: votes, save state, collection context, reviews and the metadata you need before deciding what to do next.

Institutions

Add specific reaction

Move through the context

Research map

Open full explorer

Move through nearby people, institutions, topics and adjacent work without leaving the paper page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Structured reviews

0 review(s)

ContributeLeave structured feedbackUse the review template when you have a concrete strength, concern or method question.Open review form

No structured reviews yet. High-signal critique starts here.

Work discussion

0 comment(s)

DiscussAdd a high-signal commentKeep quick notes, caveats and replication pointers separate from formal reviews.Open comment form

No discussion yet. The first strong comment sets the tone.