apalache-mc/apalache-tests

Benchmarks for apalache

apalache-tests

This repository contains various benchmarks for evaluating performance of the Apalache model checker. Many of these benchmarks are adapted from the TLA+ examples and thus are distributed under their license. Some benchmarks have their own licenses, which we kindly ask you to respect.

Performance benchmarks

See the results for inductive invariants and bounded model checking.

Parametric benchmarks

Here we collect benchmarks that can be scaled according to some parameter. They are helpful to assess how various model checking methods scale wrt. the parameter.

See the results for:

• set addition
• set addition and deletion
• set send and receive
• set send and receive with unreachable error state
• set send and receive with unreachable error state, encoded with cardinalities
• integer clocks
• integer clocks with unreachable error state

In these benchmarks we compare how symbolic approach of Apalache v. 0.7.0 behaves compared to explicit state model checking of TLC, and to the quantified SMT encoding of bounded model checking, solved with Z3. As we compare Apalache v. 0.7.0, against TLC and Z3, bundled with the current build of Apalache, we show v. 0.7.0 for those tools as well -- their actual versions are different!

Usage

Benchmarks are run via GitHub actions, configured in .github/workflows/benchmarks.yml.

New benchmarks are run automatically from the unstable branch of Apalache every Saturday.

You can manually trigger the benchmarks to run for a specific released version (or from unstable by specifying the version as unreleased) by selecting "Run workflow" from the Run Benchmarks action.

You can also specify a strategy to run. Valid strategies are listed in the ./STRATEGIES and ./ENCODING_STRATEGIES files. Additionally, you can supply the string arrays-encoding to run all strategies focused on benchmarking the experimental array-based SMT encoding.

Running the benchmarks locally

Dependencies

• Python3, including
  • matplotlib via pip install matplotlib
  • csvtomd via pip install csvtomd
  • (if you use pipenv, then just pipenv shell)
• GNU Parallel
  • Ubuntu: apt install parallel

On Mac OS

• gnu-time
• gtimeout via brew install coreutils

On Linux

• time

Running the benchmarks locally

For instructions on how to run benchmarks and generate the reports, run

make help

New reports are saved into ./results.

NOTES

• The source of truth for currently supported strategies is the file ./STRATEGIES.

Warning

These specifications should not be used for learning TLA+. We are collecting the specifications that are challenging for our model checker. These specifications are usually modified in a way that makes it easier Apalache to analyze them. So these specifications may contain bugs that were not present in the original specifications.

If you like to learn TLA+, check Leslie Lamport's TLA+ Home Page.