DOI: 10.5281/zenodo.5903160
title: Dataset for Cost-effective Simulation-based Test Selection in Self-driving Cars Software with SDC-Scissor

description: <strong>SDC-Scissor tool for Cost-effective Simulation-based Test Selection in Self-driving Cars Software</strong> This dataset provides test cases for self-driving cars with the BeamNG simulator. Check out the repository and demo video to get started. <strong>GitHub:</strong> github.com/ChristianBirchler/sdc-scissor This project extends the tool competition platform from the Cyber-Phisical Systems Testing Competition which was part of the SBST Workshop in 2021. <strong>Usage</strong> <strong>Demo</strong> YouTube Link <strong>Installation</strong> The tool can either be run with Docker or locally using Poetry. When running the simulations a working installation of BeamNG.research is required. Additionally, this simulation cannot be run in a Docker container but must run locally. To install the application use one of the following approaches: Docker: <code>docker build --tag sdc-scissor .</code> Poetry: <code>poetry install</code> <strong>Using the Tool</strong> The tool can be used with the following two commands: Docker: <code>docker run --volume "$(pwd)/results:/out" --rm sdc-scissor [COMMAND] [OPTIONS]</code> (this will write all files written to <code>/out</code> to the local folder <code>results</code>) Poetry: <code>poetry run python sdc-scissor.py [COMMAND] [OPTIONS]</code> There are multiple commands to use. For simplifying the documentation only the command and their options are described. Generation of tests: <code>generate-tests --out-path /path/to/store/tests</code> Automated labeling of Tests: <code>label-tests --road-scenarios /path/to/tests --result-folder /path/to/store/labeled/tests</code> <em>Note:</em> This only works locally with BeamNG.research installed Model evaluation: <code>evaluate-models --dataset /path/to/train/set --save</code> Split train and test data: <code>split-train-test-data --scenarios /path/to/scenarios --train-dir /path/for/train/data --test-dir /path/for/test/data --train-ratio 0.8</code> Test outcome prediction: <code>predict-tests --scenarios /path/to/scenarios --classifier /path/to/model.joblib</code> Evaluation based on random strategy: <code>evaluate --scenarios /path/to/test/scenarios --classifier /path/to/model.joblib</code> The possible parameters are always documented with <code>--help</code>. <strong>Linting</strong> The tool is verified the linters flake8 and pylint. These are automatically enabled in Visual Studio Code and can be run manually with the following commands: <pre>poetry run flake8 . poetry run pylint **/*.py</pre> <strong>License</strong> The software we developed is distributed under GNU GPL license. See the LICENSE.md file. <strong>Contacts</strong> Christian Birchler - Zurich University of Applied Science (ZHAW), Switzerland - birc@zhaw.ch Nicolas Ganz - Zurich University of Applied Science (ZHAW), Switzerland - gann@zhaw.ch Sajad Khatiri - Zurich University of Applied Science (ZHAW), Switzerland - mazr@zhaw.ch Dr. Alessio Gambi - Passau University, Germany - alessio.gambi@uni-passau.de Dr. Sebastiano Panichella - Zurich University of Applied Science (ZHAW), Switzerland - panc@zhaw.ch <strong>References</strong> Christian Birchler, Nicolas Ganz, Sajad Khatiri, Alessio Gambi, and Sebastiano Panichella. 2022. Cost-effective Simulation-based Test Selection in Self-driving Cars Software with SDC-Scissor. In 2022 IEEE 29th International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE. <strong>If you use this tool in your research, please cite the following papers:</strong> <pre><code>@INPROCEEDINGS{Birchler2022, author={Birchler, Christian and Ganz, Nicolas and Khatiri, Sajad and Gambi, Alessio, and Panichella, Sebastiano}, booktitle={2022 IEEE 29th International Conference on Software Analysis, Evolution and Reengineering (SANER), title={Cost-effective Simulationbased Test Selection in Self-driving Cars Software with SDC-Scissor}, year={2022}, }</code></pre>
description: ACKNOWLEDGMENTS We gratefully acknowledge the Horizon 2020 (EU Commission) support for the project COSMOS (DevOps for Complex Cyber-physical Systems), Project No. 957254-COSMOS) and the DFG project STUNT (DFG Grant Agreement n. FR 2955/4-1).