Enhancing Dependency Management with GitHub’s Dependency Graph: An Analysis


In the evolving landscape of software development, dependency management is a critical component. GitHub’s dependency graph is a feature designed to provide insights into the dependencies and dependents of software projects. Leveraged by tools like Dependabot and SBOM (Software Bill of Materials) generators, this feature holds significant promise for improving software maintenance and security. However, a recent study by Daniele Bifolco and colleagues reveals some notable inaccuracies in GitHub’s dependency graph, raising concerns about its reliability for both researchers and practitioners.

The Study

The study, conducted by researchers from the University of Sannio and the University of Salerno, aimed to assess the accuracy of GitHub’s dependency graph in Java and Python open-source projects. They randomly sampled 297 Java and 338 Python projects hosted on GitHub, analyzing their dependency graphs through three distinct methodologies: backward analysis, forward analysis, and manifest/lock file analysis.

  1. Backward Analysis: This involved checking if the dependencies listed in a target repository’s dependency graph were reciprocally listed as dependents in the source repository’s dependency graph.
  2. Forward Analysis: This focused on verifying if the dependents listed in a target repository’s dependency graph were accurately listed as dependencies in the source repository.
  3. Manifest/Lock File Analysis: This assessed the consistency between dependencies listed in the dependency graph and those specified in the project’s manifest or lock files.


The results highlighted several inaccuracies in GitHub’s dependency graph:

  • Backward Analysis: 75.17% of dependencies for Java projects and 84.48% for Python projects were correctly detected. The main issues included missing occurrences, incorrect package links, non-existent source repositories, and missing links to source repositories.
  • Forward Analysis: 96.62% of dependents for Java projects and 70.94% for Python projects were correctly identified. Issues here mirrored those in the backward analysis, with a significant proportion of missing occurrences.
  • Manifest/Lock File Analysis: For Java projects, only 77.19% of dependencies matched those in the manifest/lock files, while Python projects had a higher match rate of 95.71%. Discrepancies were often due to inconsistencies in version tags and missing dependencies in the dependency graph.


The inaccuracies found in GitHub’s dependency graph have several implications:

  • For Researchers: Studies relying on GitHub’s dependency graph should validate the data independently to avoid skewed results. Researchers may need to develop more accurate dependency analyzers to complement GitHub’s data.
  • For Practitioners: Tools like Dependabot and SBOM generators, which rely on GitHub’s dependency graph, may provide incomplete or inaccurate information, leading to potential security vulnerabilities or licensing issues. Developers should be aware of these limitations and consider supplementary tools or manual checks.
  • For GitHub Developers: The insights from this study can guide improvements in the accuracy and reliability of the dependency graph feature. Enhancing this tool can significantly benefit the broader software development community.


While GitHub’s dependency graph is a valuable tool, this study underscores the need for caution and supplementary validation when using it for critical tasks. By addressing the highlighted inaccuracies, GitHub can enhance its utility, making dependency management more reliable and secure for developers worldwide.

Future Work

The researchers plan to extend their study to other programming languages and investigate the impact of dependency graph inaccuracies on past studies. Continuous monitoring and analysis will be essential to track improvements in GitHub’s dependency graph accuracy over time.

By recognizing and addressing these challenges, we can improve dependency management practices, ensuring more robust and secure software development processes.

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