Table of Contents:
LintQ should work on any machine as long as docker can be installed on it. LintQ rely on CodeQL which has different computing requirements depending on the size of the dataset of programs under analysis.
- RAM: 16+ GB
We tested it with:
- OS: Ubuntu 22.04.4 LTS
- CPU: Intel(R) Xeon(R) Gold 6230 CPU @ 2.10GHz
- Cores: 16
- RAM: 32 GB
- RAM: 64+ GB
We tested it with:
- OS: Ubuntu 20.04.6 LTS
- CPU: Intel(R) Xeon(R) Silver 4214 CPU @ 2.20GHz
- Cores: 48
- RAM: 252 GB
Note: This recommendations have been derived from the official CodeQL documentation from GitHub given that our largest dataset of 7k programs and 884k lines of code (computed with sloccount).
We distinguish between two setups:
This is recommended for anyone interested in the first try of LintQ, it requires minimal installation and setup.
- Docker (tested with versions:
24.0.2, build cb74dfc
and20.10.23, build7155243
). Rundocker --version
to check the version. If not present,install here. - Conda (tested with versions:
24.3.0
and22.9.0
). Runconda --version
to check the version. If not present, install here.
This is recommended for anyone interested in using LintQ on his/her own quantum programs when developing. These tooling will allow you to run the LintQ queries from your IDE and see the results direcly there.
- VSCode (tested with versions:
1.88.1
). Runcode --version
to check the version. If not present, install here. - SARIF Viewer Extension for VSCode (tested with versions:
3.4.4
). Available here. - [IMPORTANT PRECISE VERSION] Codeql CLI Version: 2.11.2 (PRECISELY THIS). Run
codeql version
to check the version. If not present, install here. - [IMPORTANT PRECISE VERSION] CodeQL for Visual Studio Code extension: 1.7.4 (PRECISELY THIS). Available here.