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TLA⁺ Examples

This is a repository of TLA⁺ specifications and models covering applications in a variety of fields. It serves as:

• a comprehensive example library demonstrating how to specify an algorithm in TLA⁺
• a diverse corpus facilitating development & testing of TLA⁺ language tools
• a collection of case studies in the application of formal specification in TLA⁺

All TLA⁺ specs can be found in the specifications directory. The table below lists all specs and their features, such as whether they are suitable for beginners, come with an additional PlusCal variant (✔), or use PlusCal exclusively. The manifest.json file is the source of truth for this table, and is a detailed human- & machine-readable description of the specs & their models. Its schema is manifest-schema.json. All specs in this repository are subject to CI validation to ensure quality.

Examples Included Here

Here is a list of specs included in this repository, with links to the relevant directory and flags for various features:

Examples Elsewhere

Here is a list of specs stored in locations outside this repository, including submodules. They are not covered by CI testing so it is possible they contain errors, the reported details are incorrect, or they are no longer available. Ideally these will be moved into this repo over time.

License

The repository is under the MIT license and you are encouraged to publish your spec under a similarly-permissive license. However, your spec can be included in this repo along with your own license if you wish.

Support or Contact

Do you have any questions or comments? Please open an issue or send an email to the TLA+ group.

Contributing

Do you have your own case study or TLA⁺ specification you'd like to share with the community? Follow these instructions:

1. Fork this repository and create a new directory under specifications with the name of your spec
2. Place all TLA⁺ files in the directory, along with their .cfg model files
3. You are encouraged to include at least one model that completes execution in less than 10 seconds, and (if possible) a model that fails in an interesting way - for example illustrating a system design you previously attempted that was found unsound by TLC
4. Ensure name of each .cfg file matches the .tla file it models, or has its name as a prefix; for example SpecName.tla can have the models SpecName.cfg and SpecNameLiveness.cfg, etc.
5. Consider including a README.md in the spec directory explaining the significance of the spec with links to any relevant websites or publications, or integrate this info as comments in the spec itself
6. Add an entry to the table of specs included in this repo, summarizing your spec and its attributes

Adding Spec to Continuous Integration

To combat bitrot, it is important to add your spec and model to the continuous integration system. To do this, you'll have to update the manifest.json file with machine-readable records of your spec files. If this process doesn't work for you, you can alternatively modify the .ciignore file to exclude your spec from validation. Modifying the manifest.json can be done manually or (recommended) following these directions:

1. Ensure you have Python 3.11+ installed
2. It is considered best practice to create & initialize a Python virtual environment to preserve your system package store; run python -m venv . then source ./bin/activate on Linux & macOS or .\Scripts\activate.bat on Windows (run deactivate to exit)
3. Run pip install -r .github/scripts/requirements.txt
4. Run python .github/scripts/generate_manifest.py
5. Locate your spec's entry in the manifest.json file and ensure the following fields are filled out:
   • Spec title: an appropriate title for your specification
   • Spec description: a short description of your specification
   • Spec sources: links relevant to the source of the spec (papers, blog posts, repositories)
   • Spec authors: a list of people who authored the spec
   • Spec tags:
     • "beginner" if your spec is appropriate for TLA⁺ newcomers
   • Model runtime: approximate model runtime on an ordinary workstation, in "HH:MM:SS" format
   • Model size:
     • "small" if the model can be executed in less than 10 seconds
     • "medium" if the model can be executed in less than five minutes
     • "large" if the model takes more than five minutes to execute
   • Model mode:
     • "exhaustive search" by default
     • {"simulate": {"traceCount": N}} for a simulation model
     • "generate" for model trace generation
   • Model result:
     • "success" if the model completes successfully
     • "assumption failure" if the model violates an assumption
     • "safety failure" if the model violates an invariant
     • "liveness failure" if the model fails to satisfy a liveness property
     • "deadlock failure" if the model encounters deadlock
   • (Optional) Model state count info: distinct states, total states, and state depth
     • These are all individually optional and only valid if your model uses exhaustive search and results in success
     • Recording these turns your model into a powerful regression test for TLC
   • Other fields are auto-generated by the script; if you are adding an entry manually, ensure their values are present and correct (see other entries or the manifest-schema.json file)

Before submitted your changes to run in the CI, you can quickly check your manifest.json for errors and also check it against manifest-schema.json at https://www.jsonschemavalidator.net/.