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Thus you can follow the usual contribution process in GitHub through
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[Pull Requests](https://help.github.com/articles/about-pull-requests/). You can
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see for instance the pull requests open and closed at this point in time and for
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this repository at https://github.com/innersourcecommons/managing-inner-source-projects/pulls .
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this repository at https://github.com/innersourcecommons/managing-innersource-projects/pulls .
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If you want to share an idea, comment or feature request, you can also open an issue
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in the [GitHub issues tracking for this repository](https://github.com/innersourcecommons/managing-inner-source-projects/issues).
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in the [GitHub issues tracking for this repository](https://github.com/innersourcecommons/managing-innersource-projects/issues).
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Please be aware that all of your contributions will be opened by default and
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that those will be licensed under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
@@ -23,7 +23,7 @@ This book uses the [Goal-Question-Metric] approach to documenting InnerSource me
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The full graph of relationships is visible in this [graph].
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Add in your scenarios by copying this [goal template], [question template], and/or [metric template], filling them out, and submitting them in pull request.
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After successful merge, a [Trusted Committer](https://github.com/orgs/InnerSourceCommons/teams/ispo-working-group-trusted-committers) will manually[^1] add those to the [graph].
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After successful merge, a [Trusted Committer](https://github.com/orgs/InnerSourceCommons/teams/ispo-working-group-trusted-committers) will manually add those to the [graph].
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If interested, feel free to [edit the MermaidJS source] of the graph on your own!
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By adding in your scenarios to the graph, you will be able to see how others approach and interact with them.
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When using titles in Markdown, use # for main title, ## for the second header title, etc. It's just to follow the same style :).
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[^1]: [#37](https://github.com/InnerSourceCommons/managing-inner-source-projects/issues/37) will automate the addition of new goals, questions, and metrics to the graph.
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At that time this manual step will no longer be needed.
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Detailed explanation of InnerSource topics should go in the [InnerSource Patterns] book and be linked from the article here.
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Think of the articles in this book like either a conversational introduction/summary to a pattern or possible a journey showing how multiple patterns can be used together in an InnerSource Program.
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## Building the Book Locally
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The book is built with [mdBook](https://rust-lang.github.io/mdBook/).
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You don't need to install anything manually — the helper scripts handle everything.
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**Build the book** (output goes to `book/`):
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```sh
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make build
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```
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**Live-preview with auto-reload** (opens a local web server):
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```sh
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make serve
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```
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On the first run, the scripts will automatically install Rust and mdBook into the
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repo-local `.cargo/` and `.rustup/` directories (these are git-ignored).
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Subsequent runs reuse the existing install and start immediately.
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> **Windows users:** Run these scripts in **Git Bash** (included with
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> [Git for Windows](https://gitforwindows.org/)) or in WSL.
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## Who can contribute?
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This book is a work in progress where anyone is more than welcome to contribute
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## How Do We Structure Goals, Questions, and Metrics?
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Review the [Goal-Question-Metric Approach](./measuring/gqm.md) to further understand how we structure goals, questions, and metrics.
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Review [GQM use cases and user journeys](./measuring/gqm_example/README.md) to guide their development.
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Review [GQM use cases and user journeys](measuring/gqm_example/README.md) to guide their development.
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We document goals, questions, and metrics in separate folders in the [measuring](./measuring) directory.
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We document goals, questions, and metrics in separate folders in the [measuring](./measuring/index.md) directory.
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You can browse all goals, questions, and metrics in [graph format](./measuring/use_gqm.md).
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Add in your own scenarios to the graph!
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be found at the beginning of each chapter. This initiative is fostered by the [ISPO Working Group].
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For further questions, please contact us in [Slack](https://innersourcecommons.org/slack)!
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The book cover was created by [Sebastian Spier](https://spier.hu), using an image by user [Bru-nO](https://pixabay.com/photos/measure-unit-of-measure-meterstab-2737004/), available under the [Pixabay License](https://pixabay.com/service/license/).
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The book cover was created by [Sebastian Spier](https://spier.hu), using an image by user [Bru-nO](https://pixabay.com/users/bru-no-1161770/), available under the [Pixabay License](https://pixabay.com/service/license-summary/).
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All of the content found in this repository is licensed [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
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Please see [LICENSE.txt] in this repository for the full text of the license.
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Please see [LICENSE.txt](./LICENSE.txt) in this repository for the full text of the license.
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[Bitergia]: https://bitergia.com/
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[ISPO Working Group]: https://innersourcecommons.org/community/#ispo
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contribute towards. Then communicate it and get as much air cover
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from your executives as you can.
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For how governance and transparency support responsible use of AI in development, see [InnerSource and AI](/innersource-and-ai/innersource-and-ai.md), in particular [Risks and Guardrails](/innersource-and-ai/risks-and-guardrails.md).
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For how governance and transparency support responsible use of AI in development, see [InnerSource and AI](../innersource-and-ai/innersource-and-ai.md), in particular [Risks and Guardrails](../innersource-and-ai/risks-and-guardrails.md).
Organizations are increasingly adopting AI in the workplace—from generative AI assistants to agentic coding tools that can write, refactor, and review code. This shift is changing how developers work: less time on typing code, more on defining requirements, guiding AI, and making sure systems are reliable and maintainable. For InnerSource program leads, the question is whether InnerSource still matters in this new landscape.
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Organizations are increasingly adopting AI in the workplace—from generative AI assistants to agentic coding tools that can write, refactor, and review code. In many organizations, developers are now expected to do agentic coding (sometimes called "vibe coding"), where the role shifts from writing code to providing instructions in natural language and overseeing the work of automated coding agents. Some teams are going further, with multiple agents representing roles like quality engineering, project management, and frontend/backend development working in tandem and interacting directly with tools like issue trackers and source control platforms.
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This shift raises important questions: does software reuse still matter when AI can regenerate capabilities on demand? How do you maintain quality when code is produced at unprecedented speed? For InnerSource program leads, the question is whether InnerSource still matters in this new landscape.
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It does. InnerSource is potentially *more* important than ever. Shared repositories, clear boundaries, documentation, and collaborative practices help AI systems—and the people using them—work with the right context, reuse existing components, and keep quality high. This section explains why InnerSource matters when adopting AI, how to shape your repositories and practices for AI-assisted development, and what risks and guardrails to keep in mind.
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The following articles in this section go deeper:
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-[Why InnerSource Matters When Adopting AI](why-innersource-matters-with-ai.md) — Relevance of InnerSource in an AI-augmented world, reuse, and production readiness.
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-[Shaping Repositories and Practices for AI](shaping-for-ai.md) — Repository design, documentation, and workflow integration so both humans and AI can contribute effectively.
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-[Risks and Guardrails](risks-and-guardrails.md) — Balancing speed with safety, the role of code review, and organizational best practices for AI use.
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-[Why InnerSource Matters When Adopting AI](why-innersource-matters-with-ai.md) — Relevance of InnerSource in an AI-augmented world, reuse, the future of software development, and production readiness.
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-[Shaping Repositories and Practices for AI](shaping-for-ai.md) — Repository design, documentation, agent skills, emerging standards, and workflow integration so both humans and AI can contribute effectively.
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-[Risks and Guardrails](risks-and-guardrails.md) — Balancing speed with safety, mitigating AI slop, the role of code review, and organizational best practices for AI use.
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AI tooling and practices are evolving quickly. This section will be updated as the community learns more and as survey and research data become available. If you are new to InnerSource, we recommend starting with [Getting Started with InnerSource](http://www.oreilly.com/programming/free/getting-started-with-innersource.csp) and the [Introduction](/introduction/introduction.md) to this book.
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AI tooling and practices are evolving quickly. This section will be updated as the community learns more and as survey and research data become available. If you are new to InnerSource, we recommend starting with [an Introduction to InnerSource](https://innersourcecommons.org/learn/learning-path/introduction/) and the [Introduction](../introduction/introduction.md) to this book.
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