Google’s internal analysis of open‑source software maintenance indicates that while many updates can be completed in a few engineering hours, a subset require days or even months of work. The study, titled “How much does it cost to maintain a copy of an open source software package (at Google)?”, tracked 54 package updates between April and December 2024 and surveyed engineers about the drivers of update complexity.
Scope and methodology of the Google analysis
The research focused on internal copies of open‑source packages that Google imports into its own repository. Engineers typically keep a single version of each package, and the study measured the total engineering time spent on each update, from initial assessment to final deployment. Out of the 54 updates examined, 52 involved packages written in the Go programming language. The selected updates represented less than 1 % of Google’s overall package‑update activity during the eight‑month period, reflecting internal business priorities rather than a random sample.
For each update, Google recorded the total effort in engineering hours and collected qualitative input from the engineers who performed the work. The effort metric combined time spent on code changes, testing, dependency resolution, and any downstream repairs required to keep dependent applications functional.
Key factors that extend update effort
One of the most striking findings relates to the presence of company‑specific patches. Among the 47 packages for which patch‑detection data were usable, those that carried internal modifications required a median effort three times higher than packages without such patches. Although the average effort across both groups was similar, the median highlights that a typical patched package demands substantially more work. Engineers themselves identified patches and customizations as the leading cause of additional effort.
Beyond patches, the study linked larger numbers of external dependencies and a greater count of upstream contributors to higher engineering effort. Packages that depended on many third‑party libraries or that were maintained by a broad community of contributors tended to need more time to integrate successfully. Activity within the original project’s repository—such as frequent commits or releases—also correlated with increased effort, suggesting that rapidly evolving upstream codebases can introduce more integration challenges.
Conversely, the timing of previous updates, the specific version being installed, and internal usage levels did not show a consistent relationship with update complexity. In other words, how often a package had been refreshed in the past or how widely it was used inside Google did not reliably predict the amount of work required for a new upgrade.
Remediation and repair dominate longer updates
While the median total effort across all updates was 2.2 engineering hours, the average effort rose to 13.9 hours, reflecting a long tail of high‑effort cases. One particularly demanding update was estimated at 400 hours, illustrating the potential for extreme outliers.
For updates that extended beyond two days, the majority of time was spent on remediation, according to Sophia Vargas of Google Open Source. Remediation involves resolving downstream dependency conflicts and repairing breakage that emerges when the updated package interacts with other components. An update can cascade into applications, services, or libraries that rely on the package, necessitating fixes before the new version can be considered stable.
Despite the complexity of remediation, engineers reported that familiarity with the programming language was not a barrier. In 52 of the 54 updates, engineers were very familiar with the language (primarily Go) but had little or no prior experience with the specific package or its internal usage at Google. Learning enough to begin work typically required less than one hour, indicating that the steep effort curves were driven more by integration and repair tasks than by language learning.
Limitations and context of the findings
Google cautions that the observed effort reflects its own tooling, processes, and repository structure, which may differ from practices at other organizations. The company generally maintains a single version of each imported package, and its internal tools influence the amount of work needed to repair affected software. The study deliberately avoided prescribing specific tools or evaluating the impact of emerging technologies such as generative AI on the maintenance workflow.
Additionally, the analysis excluded “stale” or archived packages, limiting coverage of older or less actively maintained libraries. Because the sample represented a small fraction of total updates and was chosen based on internal business priorities, the results should not be generalized without considering differing organizational contexts.
Nevertheless, the data provide concrete evidence that local customizations, extensive dependency trees, and active upstream development are primary contributors to prolonged update cycles. Organizations that rely heavily on open‑source components may benefit from minimizing bespoke patches, consolidating dependencies, and allocating dedicated remediation resources for high‑risk upgrades.
The study’s insights align with broader industry observations that open‑source software, while cost‑effective, still demands disciplined engineering effort to keep secure and functional. As companies continue to embed open‑source libraries deep within their technology stacks, understanding the hidden costs of maintenance becomes essential for budgeting, risk management, and long‑term sustainability.
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