Running CodeQL code scanning in your CI system

Using CodeQL code scanning with your existing CI system

If you use a continuous integration or continuous delivery/deployment (CI/CD) system other than GitHub Actions, you can use your existing system to run GitHub's CodeQL analysis and upload the results to GitHub. To do this, use the CodeQL runner.

About the CodeQL runner

Code scanning is a feature that you use to analyze the code in a GitHub repository to find security vulnerabilities and coding errors. Any problems identified by the analysis are shown in GitHub. For information, see "About code scanning."

You can use the CodeQL runner to run code scanning on code that you're processing in a third-party continuous integration (CI) system. Alternatively, you can use GitHub Actions to run code scanning on GitHub. For information, see "Enabling code scanning for a repository."

The CodeQL runner is a command-line tool that runs CodeQL analysis on a checkout of a GitHub repository. You add the runner to your third-party system, then call the runner to analyze code and upload the results to GitHub. These results are displayed as code scanning alerts in the repository.

Downloading the CodeQL runner

You can download the CodeQL runner from https://github.com/github/codeql-action/releases. On some operating systems, you may need to change permissions for the downloaded file before you can run it.

On Linux:

chmod +x codeql-runner-linux

On MacOS:

chmod +x codeql-runner-macos
sudo xattr -d com.apple.quarantine codeql-runner-macos

On Windows, the codeql-runner-win.exe file usually requires no change to permissions.

Adding the CodeQL runner to your CI system

Once you have downloaded the CodeQL runner and verified that it can be executed, you should make the runner available to each CI server that you intend to use for code scanning. It is important to notice that each CI server that you intend to use for code scanning needs to have the CodeQL runner. You might configure each server to copy the runner from a central, internal location, or you could use the REST API to get the runner direct from GitHub, for example:

wget https://github.com/github/codeql-action/releases/download/codeql-bundle-20200826/codeql-runner-linux 
chmod +x codeql-runner-linux

In addition to this, each CI server also needs:

• A GitHub Apps or personal access token for the CodeQL runner to use. For private repositories the token must have the repo scope. For public the token needs only the public_repo and repo:security_events scopes. For information, see "Building GitHub Apps" and "Creating a personal access token."
• Access to the CodeQL bundle associated with this release of the CodeQL runner. This package contains the CodeQL CLI, queries, and libraries needed for CodeQL analysis. For information, see "CodeQL CLI."

The options for providing access to the CodeQL bundle are:

1. Allow the CI servers access to GitHub.com so that the CodeQL runner can download the bundle automatically.
2. Manually download/extract the bundle, store it with other central resources, and use the --codeql-path flag to specify the location of the bundle in calls to initialize the CodeQL runner.

Calling the CodeQL runner

You should call the CodeQL runner from the checkout location of the repository you want to analyze. The two main commands are:

1. init required to initialize the runner and create a CodeQL database for each language to be analyzed. These databases are populated and analyzed by subsequent commands.
2. analyze required to populate the CodeQL databases, analyze them, and upload results to GitHub.

For both commands, you must specify the URL of GitHub, the repository OWNER/NAME, and the GitHub Apps or personal access token to use for authentication. You also need to specify the location of the CodeQL bundle unless the CI server has access to download it directly from the github/codeql-action repository on GitHub.com.

You can configure where the CodeQL runner stores the CodeQL bundle for future analysis on a server using the --tools-dir flag and where it stores temporary files during analysis using --temp-dir.

To view the command-line reference for the runner, use the -h flag. For example, to list all commands run: codeql-runner-OS -h, or to list all the flags available for the init command run: codeql-runner-OS init -h (where OS varies according to the executable that you are using). For more information, see "Configuring code scanning in your CI system."

Basic example

This example runs CodeQL analysis on a Linux CI server for the octo-org/example-repo repository hosted on https://github.com. The process is very simple because the repository contains only languages that can be analyzed by CodeQL directly, without being built (that is, Go, JavaScript, Python, and TypeScript).

1. Check out the repository to analyze.

2. Move into the directory where the repository is checked out.

3. Initialize the CodeQL runner and create CodeQL databases for the languages detected.

   $ /path/to-runner/codeql-runner-linux init --repository octo-org/example-repo
       --github-url https://github.com --github-auth TOKEN
   > Cleaning temp directory /srv/checkout/example-repo/codeql-runner
   > ...
   > Created CodeQL database at /srv/checkout/example-repo/codeql-runner/codeql_databases/javascript.

4. Populate the CodeQL runner databases, analyze them, and upload the results to GitHub.

   $ /path/to-runner/codeql-runner-linux analyze --repository octo-org/example-repo
       --github-url https://github.com --github-auth TOKEN
       --commit 5b6a3078b31dc346e5ce7b86837d6abbe7a18bbd --ref refs/heads/main
   > Finalizing database creation
   > ...
   > POST /repos/octo-org/example-repo/code-scanning/sarifs - 202 in 786ms
   > Successfully uploaded results

The server has access to download the CodeQL bundle directly from the github/codeql-action repository on GitHub.com, so there is no need to use the --codeql-path flag. When the analysis is complete, the CodeQL runner uploads the results to the code scanning view. For more information, see "Managing code scanning alerts for your repository."

Compiled language example

This example is similar to the previous example, however this time the repository has code in C/C++, C#, or Java. To create a CodeQL database for these languages, the CLI needs to monitor the build. At the end of the initialization process, the runner reports the command you need to set up the environment before building the code. You need to run this command, before calling the normal CI build process, and then running the analyze command.

1. Check out the repository to analyze.

2. Move into the directory where the repository is checked out.

3. Initialize the CodeQL runner and create CodeQL databases for the languages detected.

   $ /path/to-runner/codeql-runner-linux init --repository octo-org/example-repo-2
       --github-url https://github.com --github-auth TOKEN
   > Cleaning temp directory /srv/checkout/example-repo-2/codeql-runner
   > ...
   > CodeQL environment output to "/srv/checkout/example-repo-2/codeql-runner/codeql-env.json"
     and "/srv/checkout/example-repo-2/codeql-runner/codeql-env.sh".
     Please export these variables to future processes so the build can be traced, for example by running "
     . /srv/checkout/example-repo-2/codeql-runner/codeql-env.sh".

4. Run the script generated by the init action to set up the environment to monitor the build.

   $ . /srv/checkout/example-repo-2/codeql-runner/codeql-env.sh

5. Build the code.

6. Populate the CodeQL databases, analyze them, and upload the results to GitHub.

   $ /path/to-runner/codeql-runner-linux analyze --repository octo-org/example-repo-2
       --github-url https://github.com --github-auth TOKEN
       --commit ae7b655ef30b50fb726ae7b3daa79571a39d194d --ref refs/heads/main
   > Finalizing database creation
   > ...
   > POST /repos/octo-org/example-repo-2/code-scanning/sarifs - 202 in 573ms
   > Successfully uploaded results

Further reading

• "Configuring code scanning in your CI system"
• "Troubleshooting code scanning in your CI system"