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2025-02-13
GitHub has recently made a notable update to its CodeQL security analysis, offering users more flexibility in how their analysis is run. This improvement allows you to specify whether the analysis should run on a standard GitHub-hosted runner, a larger GitHub-hosted runner, or even a self-hosted runner. This update provides more control over the resources used during code scanning, which can improve both performance and efficiency.
Previously, support for larger GitHub-hosted and self-hosted runners was only available with a custom label specific to code scanning. Now, you can apply any custom label, enabling you to assign the analysis to the most suitable machine for the task. This is a significant step forward, allowing for more powerful runners to be allocated to critical repositories, speeding up the analysis process, and improving the distribution of workload across both GitHub-hosted and self-hosted runners. Additionally, you can also configure the analysis to run on specific platforms, such as macOS.
This new setting is already available on GitHub.com, and it can be configured both at the repository level and within code security configurations for large-scale deployments. It will also be included in GitHub Enterprise Server (GHES) version 3.16.
What Undercode Says:
This update from GitHub brings a lot of potential benefits for developers who rely on CodeQL for security analysis. Previously, there was a limitation in the flexibility and scalability of GitHub-hosted runners, and while self-hosted runners offered some solution, configuring them was not as streamlined. With the new option to specify custom labels for code scanning, GitHub is clearly making a move towards offering developers better control over their security processes.
One of the key advantages of this change is that developers can now assign more powerful, specialized runners to critical repositories. This could significantly speed up security scans, especially for large projects that require more resources to analyze effectively. For instance, the use of larger runners or more powerful self-hosted machines can accelerate scans, ensuring that security vulnerabilities are identified faster, which is crucial in today’s fast-paced development environments.
Furthermore, distributing the workload across different types of runners—whether hosted by GitHub or self-hosted—can lead to more balanced performance. This ensures that the system remains responsive, even during high-demand times. The flexibility to assign custom labels based on specific criteria, such as repository importance or platform requirements, provides an extra layer of customization, allowing teams to adapt their security scans to their exact needs.
Another critical aspect is the ability to choose the platform for running the analysis. Running CodeQL on different platforms, like macOS, could be crucial for projects that are tailored for those environments. It’s a level of control that enhances the adaptability of the security scanning process. As different platforms can behave differently with code, analyzing them separately ensures more accurate detection of vulnerabilities that might only appear on certain systems.
For large-scale deployments, the ability to configure this at the code security level adds an element of scalability, making it easier to maintain security standards across a vast number of repositories. For organizations using GitHub Enterprise Server (GHES), the inclusion of this feature in version 3.16 makes it even more valuable for teams working in larger, enterprise-level projects.
Ultimately,
References:
Reported By: https://github.blog/changelog/2024-12-03-improved-pull-request-merge-experience-now-in-public-preview/
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