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Troubleshooting the CodeQL workflow

If you're having problems with escaneo de código, you can troubleshoot by using these tips for resolving issues.

Escaneo de código is available in public repositories, and in private repositories owned by organizations with an Advanced Security license. Para obtener más información, consulta la sección "Productos de GitHub".

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Nota: Escaneo de código se encuentra acutalmente en beta y está sujeto a cambios. To request access to the beta, join the waitlist.

Automatic build for a compiled language fails

If an automatic build of code for a compiled language within your project fails, try the following troubleshooting steps.

  • Remove the autobuild step from your escaneo de código workflow and add specific build steps. For information about editing the workflow, see "Configuring escaneo de código." For more information about replacing the autobuild step, see "Configuring the CodeQL workflow for compiled languages."

  • If your workflow doesn't explicitly specify the languages to analyze, CodeQL implicitly detects the supported languages in your code base. In this configuration, out of the compiled languages C/C++, C#, and Java, CodeQL only analyzes the language with the most source files. Edit the workflow and add a build matrix specifying the languages you want to analyze. The default CodeQL analysis workflow uses such a matrix.

    The following extracts from a workflow show how you can use a matrix within the job strategy to specify languages, and then reference each language within the "Initialize CodeQL" step:

    jobs:
      analyze:
        ...
        strategy:
          fail-fast: false
          matrix:
            language: ['csharp', 'cpp', 'javascript']
    
        ...
    
        - name: Initialize CodeQL
          uses: github/codeql-action/init@v1
          with:
            languages: ${{ matrix.language }}
    

    For more information about editing the workflow, see "Configuring code scanning."

No code found during the build

If your workflow fails with an error No source code was seen during the build or The process '/opt/hostedtoolcache/CodeQL/0.0.0-20200630/x64/codeql/codeql' failed with exit code 32, this indicates that CodeQL was unable to monitor your code. Several reasons can explain such a failure:

  1. Automatic language detection identified a supported language, but there is no analyzable code of that language in the repository. A typical example is when our language detection service finds a file associated with a particular programming language like a .h, or .gyp file, but no corresponding executable code is present in the repository. To solve the problem, you can manually define the languages you want to analyze by updating the list of languages in the language matrix. For example, the following configuration will analyze only Go, and JavaScript.

    strategy:
      fail-fast: false
      matrix:
        # Override automatic language detection by changing the list below
        # Supported options are:
        # ['csharp', 'cpp', 'go', 'java', 'javascript', 'python']
        language: ['go', 'javascript']
    

    For more information, see the workflow extract in "Automatic build for a compiled language fails" above.

  2. Your escaneo de código workflow is analyzing a compiled language (C, C++, C#, or Java), but the code was not compiled. By default, the CodeQL analysis workflow contains an autobuild step, however, this step represents a best effort process, and may not succeed in building your code, depending on your specific build environment. Compilation may also fail if you have removed the autobuild step and did not include build steps manually. For more information about specifying build steps, see "Configuring the CodeQL workflow for compiled languages."

  3. Your workflow is analyzing a compiled language (C, C++, C#, or Java), but portions of your build are cached to improve performance (most likely to occur with build systems like Gradle or Bazel). Since CodeQL observes the activity of the compiler to understand the data flows in a repository, CodeQL requires a complete build to take place in order to perform analysis.

  4. Your workflow is analyzing a compiled language (C, C++, C#, or Java), but compilation does not occur between the init and analyze steps in the workflow. CodeQL requires that your build happens in between these two steps in order to observe the activity of the compiler and perform analysis.

  5. Your compiled code (in C, C++, C#, or Java) was compiled successfully, but CodeQL was unable to detect the compiler invocations. The most common causes are certain configuration options like running your build process in a container, if you're building using a distributed build system external to GitHub Actions using a daemon process, or if CodeQL isn't aware of the specific compiler you are using.

    For C# projects using either dotnet build or msbuild which target .NET Core 2, you should specify /p:UseSharedCompilation=false in your workflow's run step, when you build your code. The UseSharedCompilation flag isn't necessary for .NET Core 3.0 and later.

    For example, the following configuration for C# will pass the flag during the first build step.

    - run: |
        dotnet build /p:UseSharedCompilation=false 
    

    If you encounter another problem with your specific compiler or configuration, contact GitHub Support o GitHub Premium Support.

For more information about specifying build steps, see "Configuring the CodeQL workflow for compiled languages."

Portions of my repository were not analyzed using autobuild

The CodeQL autobuild feature uses heuristics to build the code in a repository, however, sometimes this approach results in incomplete analysis of a repository. For example, when multiple build.sh commands exist in a single repository, the analysis may not complete since the autobuild step will only execute one of the commands. The solution is to replace the autobuild step with build steps which build all of the source code which you wish to analyze. For more information, see "Configuring the CodeQL workflow for compiled languages."

Error: "Server error"

If the run of a workflow for escaneo de código fails due to a server error, try running the workflow again. If the problem persists, contact GitHub Support o GitHub Premium Support.

Error: "Out of disk" or "Out of memory"

On very large projects, CodeQL may run out of disk or memory on the runner. If you encounter this issue on a hosted GitHub Actions runner, contact GitHub Support o GitHub Premium Support so that we can investigate the problem.

The build takes too long

If your build with CodeQL analysis takes too long to run, there are several approaches you can try to reduce the build time.

Increase the memory or cores

If you use self-hosted runners to run CodeQL analysis, you can increase the memory or the number of cores on those runners.

Use matrix builds to parallelize the analysis

The default CodeQL Analysis workflow uses a build matrix of languages, which causes the analysis of each language to run in parallel. If you have specified the languages you want to analyze directly in the "Initialize CodeQL" step, analysis of each language will happen sequentially. To speed up analysis of multiple languages, modify your workflow to use a matrix. For more information, see the workflow extract in "Automatic build for a compiled language fails" above.

Reduce the amount of code being analyzed in a single workflow

Analysis time is typically proportional to the amount of code being analyzed. You can reduce the analysis time by reducing the amount of code being analyzed at once, for example, by excluding test code, or breaking analysis into multiple workflows that analyze only a subset of your code at a time.

For compiled languages like Java, C, C++, and C#, CodeQL analyzes all of the code which was built during the workflow run. To limit the amount of code being analyzed, build only the code which you wish to analyze by specifying your own build steps in a run block. You can combine specifying your own build steps with using the paths or paths-ignore filters on the pull_request and push events to ensure that your workflow only runs when specific code is changed. For more information, see "Workflow syntax for GitHub Actions."

For interpreted languages like Go, JavaScript, Python, and TypeScript, that CodeQL analyzes without a specific build, you can specify additional configuration options to limit the amount of code to analyze. For more information, see "Specifying directories to scan."

If you split your analysis into multiple workflows as described above, we still recommend that you have at least one workflow which runs on a schedule which analyzes all of the code in your repository. Because CodeQL analyzes data flows between components, some complex security behaviors may only be detected on a complete build.

Run only during a schedule event

If your analysis is still too slow to be run during push or pull_request events, then you may want to only trigger analysis on the schedule event. For more information, see "Events."

Results differ between analysis platforms

If you are analyzing code written in Python, you may see different results depending on whether you run the CodeQL Analysis workflow on Linux, macOS, or Windows.

On GitHub-hosted runners that use Linux, the CodeQL Analysis workflow tries to install and analyze Python dependencies, which could lead to more results. To disable the auto-install, add setup-python-dependencies: false to the "Initialize CodeQL" step of the workflow. For more information about configuring the analysis of Python dependencies, see "Analyzing Python dependencies."

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