Replication package for Evaluating Feature-Model Collection (SPLC'24)

Recommended usage with Docker

We provide a docker image for our empirical evaluation. To replicate the evaluation follow the steps below.

1. Login to ghcr. Find instructions here, even though the package is public log in seems neccessary.

2. Get image: docker pull ghcr.io/sundermannc/fmbenchmarkeval:v1

3. Run analyses:

   docker run --name fmbenchmarksharpsat -d ghcr.io/sundermannc/fmbenchmarkeval:v1 run_configurations/sharpsat.yaml

   docker run --name fmbenchmarksat -d ghcr.io/sundermannc/fmbenchmarkeval:v1 run_configurations/sat.yaml

4. Get results:

docker cp fmbenchmarksharpsat:benchmark/results/ results-sharpsat

docker cp fmbenchmarksat:benchmark/results/ results-sat

Without docker

Disclaimer: We recommend using the docker container since setting up all solvers can be tricky, especially on non-Linux systems.

Binaries

We only provide Linux binaries for the different solvers. As the different solvers depend on various Linux-only libraries, we recommend to use a Linux-based system or VM.

Steps to reproduce

1. Get feature models from the feature-model collection. The models are not duplicated here due to their sizes.
2. Copy dimacs models (path/to/collection/feature_models/dimacs) to this directory: Expected paths for the script: dimacs/domain/system/modelname.dimacs
3. Run evaluation SAT: python3 run.py run_configurations/sat.yaml #SAT: python3 run.py run_configurations/sharpsat.yaml