Add CodeQL workflow for GitHub code scanning

[lgtm-com]

Hi prest/prest-node!

This is a one-off automatically generated pull request from LGTM.com 🤖. You might have heard that we’ve integrated LGTM’s underlying CodeQL analysis engine natively into GitHub. The result is GitHub code scanning!

With LGTM fully integrated into code scanning, we are focused on improving CodeQL within the native GitHub code scanning experience. In order to take advantage of current and future improvements to our analysis capabilities, we suggest you enable code scanning on your repository. Please take a look at our blog post for more information.

This pull request enables code scanning by adding an auto-generated codeql.yml workflow file for GitHub Actions to your repository — take a look! We tested it before opening this pull request, so all should be working ✔️. In fact, you might already have seen some alerts appear on this pull request!

Where needed and if possible, we’ve adjusted the configuration to the needs of your particular repository. But of course, you should feel free to tweak it further! Check this page for detailed documentation.

Questions? Check out the FAQ below!

FAQ

Click here to expand the FAQ section

How often will the code scanning analysis run?

By default, code scanning will trigger a scan with the CodeQL engine on the following events:

• On every pull request — to flag up potential security problems for you to investigate before merging a PR.
• On every push to your default branch and other protected branches — this keeps the analysis results on your repository’s Security tab up to date.
• Once a week at a fixed time — to make sure you benefit from the latest updated security analysis even when no code was committed or PRs were opened.

What will this cost?

Nothing! The CodeQL engine will run inside GitHub Actions, making use of your unlimited free compute minutes for public repositories.

What types of problems does CodeQL find?

The CodeQL engine that powers GitHub code scanning is the exact same engine that powers LGTM.com. The exact set of rules has been tweaked slightly, but you should see almost exactly the same types of alerts as you were used to on LGTM.com: we’ve enabled the security-and-quality query suite for you.

How do I upgrade my CodeQL engine?

No need! New versions of the CodeQL analysis are constantly deployed on GitHub.com; your repository will automatically benefit from the most recently released version.

The analysis doesn’t seem to be working

If you get an error in GitHub Actions that indicates that CodeQL wasn’t able to analyze your code, please follow the instructions here to debug the analysis.

How do I disable LGTM.com?

If you have LGTM’s automatic pull request analysis enabled, then you can follow these steps to disable the LGTM pull request analysis. You don’t actually need to remove your repository from LGTM.com; it will automatically be removed in the next few months as part of the deprecation of LGTM.com (more info here).

Which source code hosting platforms does code scanning support?

GitHub code scanning is deeply integrated within GitHub itself. If you’d like to scan source code that is hosted elsewhere, we suggest that you create a mirror of that code on GitHub.

How do I know this PR is legitimate?

This PR is filed by the official LGTM.com GitHub App, in line with the deprecation timeline that was announced on the official GitHub Blog. The proposed GitHub Action workflow uses the official open source GitHub CodeQL Action. If you have any other questions or concerns, please join the discussion here in the official GitHub community!

I have another question / how do I get in touch?

Please join the discussion here to ask further questions and send us suggestions!

[pull-request-quantifier-deprecated]

This PR has 34 quantified lines of changes. In general, a change size of upto 200 lines is ideal for the best PR experience!

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Quantification details

Label      : Extra Small
Size       : +34 -0
Percentile : 13.6%

Total files changed: 1

Change summary by file extension:
.yml : +34 -0

Change counts above are quantified counts, based on the PullRequestQuantifier customizations.

Why proper sizing of changes matters

Optimal pull request sizes drive a better predictable PR flow as they strike a
balance between between PR complexity and PR review overhead. PRs within the
optimal size (typical small, or medium sized PRs) mean:

• Fast and predictable releases to production:
  • Optimal size changes are more likely to be reviewed faster with fewer
    iterations.
  • Similarity in low PR complexity drives similar review times.
• Review quality is likely higher as complexity is lower:
  • Bugs are more likely to be detected.
  • Code inconsistencies are more likely to be detected.
• Knowledge sharing is improved within the participants:
  • Small portions can be assimilated better.
• Better engineering practices are exercised:
  • Solving big problems by dividing them in well contained, smaller problems.
  • Exercising separation of concerns within the code changes.

What can I do to optimize my changes

• Use the PullRequestQuantifier to quantify your PR accurately
  • Create a context profile for your repo using the context generator
  • Exclude files that are not necessary to be reviewed or do not increase the review complexity. Example: Autogenerated code, docs, project IDE setting files, binaries, etc. Check out the Excluded section from your prquantifier.yaml context profile.
  • Understand your typical change complexity, drive towards the desired complexity by adjusting the label mapping in your prquantifier.yaml context profile.
  • Only use the labels that matter to you, see context specification to customize your prquantifier.yaml context profile.
• Change your engineering behaviors
  • For PRs that fall outside of the desired spectrum, review the details and check if:
    • Your PR could be split in smaller, self-contained PRs instead
    • Your PR only solves one particular issue. (For example, don't refactor and code new features in the same PR).

How to interpret the change counts in git diff output

• One line was added: +1 -0
• One line was deleted: +0 -1
• One line was modified: +1 -1 (git diff doesn't know about modified, it will
  interpret that line like one addition plus one deletion)
• Change percentiles: Change characteristics (addition, deletion, modification)
  of this PR in relation to all other PRs within the repository.

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