Add CONTRIBUTING.md with development instructions

[Copilot]

This PR addresses the issue where CI workflow outputs provide incomplete instructions for reproducing test failures locally, and reorganizes the documentation structure based on feedback.

Problem

The current CI-generated messages show commands like:

import Pkg; Pkg.test(;coverage=true, julia_args=["--check-bounds=yes", "--compiled-modules=yes", "--depwarn=yes"], force_latest_compatible_version=false, allow_reresolve=true)

But these are missing critical components needed to actually reproduce the CI environment locally:

• The --project=. flag to use the correct project environment
• Environment variables like GROUP and AD that control which tests run
• Proper command structure for shell execution

Changes Made

README.md

Simplified the README by moving all development instructions to CONTRIBUTING.md and replacing them with a concise link. This keeps the README focused on the package description while directing developers to the appropriate place for detailed instructions.

CONTRIBUTING.md

Created a comprehensive development guide including:

• Step-by-step setup instructions
• Detailed test reproduction commands for all CI scenarios with correct syntax:

  # Interface tests (reproduces "Interface tests" CI workflow)
  julia --project=. -e "ENV[\"GROUP\"] = \"Interface\"; using Pkg; Pkg.test()"
  
  # AD tests with specific backend (reproduces "AD tests" CI workflow)
  julia --project=. -e "ENV[\"GROUP\"] = \"AD\"; ENV[\"AD\"] = \"ReverseDiff\"; using Pkg; Pkg.test()"

• All currently supported AD backends (ForwardDiff, ReverseDiff, Enzyme, Mooncake)
• Basic functionality validation examples
• Code formatting and documentation building instructions with JuliaFormatter v1.x specification
• Important notes about test timing and expected failures (e.g., Enzyme on some systems)

Code Quality

Applied JuliaFormatter to improve code consistency across several test files.

Validation

All documented commands have been tested and verified to work correctly. The basic functionality examples execute successfully and produce expected results.

Now when developers encounter CI failures, they have clear, working instructions to reproduce the exact test scenarios locally using the proper project environment and configuration, all organized in the standard CONTRIBUTING.md location.

Fixes #397.

---

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[github-actions]

Bijectors.jl documentation for PR #413 is available at:
https://TuringLang.github.io/Bijectors.jl/previews/PR413/

[yebai]

@copilot, Tracker and Zygote support has been removed in #409

[Copilot]

@copilot, Tracker and Zygote support has been removed in #409

Updated! Removed all Tracker references from CONTRIBUTING.md since support was removed in #409. The AD backend examples and CI test matrix documentation now only include the currently supported backends: ForwardDiff, ReverseDiff, Enzyme, and Mooncake. (4713e64)

[AoifeHughes]

Need to specify using the version 1 formatter.

[Copilot]

Updated the CONTRIBUTING.md to specify JuliaFormatter version 1 in the installation command using PackageSpec(name="JuliaFormatter", version="1"). (62fa9e8)

[AoifeHughes]

From talking to @mhauru, @yebai and reading https://docs.github.com/en/enterprise-cloud@latest/copilot/tutorials/coding-agent/get-the-best-results here are some thoughts to build upon this current PR:

• A .github/copilot-setup-steps.yml file could/should be added
• Validation scripts could also be added .github/validation-scripts/
• Templates specifically for co-pilot use could also be added .github/ISSUE_TEMPLATE/copilot_task.md

I've spent a bit of time going through some of the potential approaches, though so as to address what is wanted / needed my main suggestion would be to create an issue (maybe even on Turing.jl main repo or a larger discussion in the best place for this sort of thing) and for us to clearly define what:

1. Our ideal use-case for co-pilot interactions will be
2. If we want core development team to use it, or if we want anyone contributing to make active use of things (at which point it would be a good sub-task / issue to create good docs in the docs repo for this)
   • It would also be an idea to update either GitHub templates or contributing docs to be explicit in the tools some (all?) of us may be expected or suggested to use
   • Slight side note: I'd like to revisit Automate formatter on PRs Turing.jl#2596 if we are wanting co-pilot or other agents to be making changes to the code base
3. Most likely not something needing too much consideration, but there are many tools and agents, do we want to dedicate time specifically to a GitHub focused setup? Would there be any benefit in making a generailised AI-Agent Instructions.md to be more tool-agnostic?

As I mentioned it might also be an idea to make sure we have an open discussion on how people feel about using these tools so routinely

• Some developers may have (very valid) concerns about encouraging AI use: https://news.mit.edu/2025/explained-generative-ai-environmental-impact-0117
• As such maybe strict definitions of when and how to use so as not to use on tasks which are frivolous
• I'm also acutely aware that some developers aren't comfortable with agents in their workflow. Again, I can't speak to but think these opinions should be gathered (for context, I have no strong feelings, just want to respect those who contribute).

I'm super happy to help with implementation or following up with ideas, suggestions or to research more into this, I just need the goals clearly defined by the project owner (@yebai)

Oh and because I've not actually used Github's direct co-pilot lets give it a whirl: @copilot do you have any thoughts on what I've said or things to add / I've missed in this comment re this current PR.

[Copilot]

From talking to @mhauru, @yebai and reading https://docs.github.com/en/enterprise-cloud@latest/copilot/tutorials/coding-agent/get-the-best-results here are some thoughts to build upon this current PR:...

Those are excellent strategic considerations! The .github/copilot-setup-steps.yml and validation scripts would definitely improve the developer experience. Your points about environmental impact, developer preferences, and tool-agnostic approaches are particularly thoughtful. This PR focused on the immediate issue #397 (incomplete CI reproduction instructions), but your suggestions would make great follow-up issues for broader Copilot integration strategy. Creating a dedicated discussion or issue for defining ideal use-cases and team preferences seems like the right next step.

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If you need me to access, download, or install something from one of these locations, you can either:

• Configure Actions setup steps to set up my environment, which run before the firewall is enabled
• Add the appropriate URLs or hosts to the custom allowlist in this repository's Copilot coding agent settings (admins only)

[AoifeHughes]

I also note that co-pilot has made significant changes to the comments it initially made ~ I'm worried this could be a documentation issue from this tools use and a limitation to consider.

[mhauru]

@yebai seems to have been trying copilot out over the last few weeks, so he probably has more thoughts. My proposal would be to pick one of our repos as a test case, add the extra files that copilot would benefit from, and then experiment with it to get an idea of what it can and can't do. If it goes well and seem useful, then expand to other repos and put in documentation for how we want to use it. If not so well then delete the files and forget the whole thing.

[yebai]

Thanks @mhauru and @AoifeHughes for looking into this.

Copilot often needs to view the codebase for its "thought" process. However, viewing the codebase itself is a slow process, so Copilot benefits from reading the code once, then saves the "understanding" of the Copilot's file.

My experience with Copilot so far is that it heavily relies on test-driving coding. This means if Copilot can make good use of the test suite (eg, selectively run a subset of tests quickly), it can often come up with a working solution.

For docs, we need to tell Copilot how to compile the docs after proposing changes to some parts of the docs.

Our ideal use-case for copilot interactions will be

If one creates an issue with a clear description of the issue and some potential ideas on how to fix it, but doesn't have the time to fix it. Then, Copilot can explore approaches to fix it. Sometimes it comes with a good solution, which can be reviewed, then improved and merged. In most other cases, Copilot conducts some useful explorations, and humans review the explorations Copilot has done to help debug the problem or develop a solution.

Most likely not something needing too much consideration, but there are many tools and agents, do we want to dedicate time specifically to a GitHub focused setup? Would there be any benefit in making a generailised AI-Agent Instructions.md to be more tool-agnostic?

Let's stay with Copilot for the initial stage to get a more concrete understanding of the coding agent's capabilities and limitations. Different coding agents are quite similar despite some minor differences.

If we want core development team to use it, or if we want anyone contributing to make active use of things

Likely for researchers or layman contributors, who want to help but don't always have the time. For example, I can work together with Copilot by providing a clear task description and potential ideas on how to fix it, thereby avoiding the need to dedicate hours to coding and debugging.

My proposal would be to pick one of our repos as a test case, add the extra files that copilot would benefit from, and then experiment with it to get an idea of what it can and can't do. If it goes well and seem useful, then expand to other repos and put in documentation for how we want to use it. If not so well then delete the files and forget the whole thing.

Seconded. Bijectors.jl is a reasonable starting point.