How to Quantify the Health of an Open Source Project

The question beyond functionality

During technology selection, teams carefully compare features, performance and API design. One question is rarely evaluated systematically: will anyone still be maintaining this project in three years?

This is not idle worry. Once a component enters your dependency tree, its maintenance status becomes your risk. An abandoned project means new vulnerabilities never get patched, newer runtimes stop being supported, and when a security incident hits you are on your own. Migration cost is almost always far higher than the two extra days evaluation would have taken.

That is where health assessment earns its place — what it predicts is how long after a vulnerability appears a patch will exist, and that number directly determines your exposure.

Four measurable categories

### 1. Release and commit cadence

The most basic activity signal, but what matters is not absolute frequency — it is regularity and trend.

• How long since the last release? More than a year is usually a clear warning (unless it is one of those genuinely finished small utilities).
• Are release intervals stable, or did they go from monthly to twice a year?
• Is the commit curve steady, growing, or in sustained decline?

Distinguish “mature and stable” from “effectively abandoned.” The test: are issues and PRs still getting responses? A project that stopped releasing but still answers questions carries very different risk from one where every channel has gone silent.

### 2. Contributor concentration (bus factor)

The most overlooked category, and the most predictive.

Bus factor asks: how many core contributors would have to leave simultaneously for the project to stall? A value of 1 means the entire project depends on one person.

Measurable dimensions include: the share of total commits from the top three contributors over the past year, the number of people with commit access, and whether maintainers are affiliated with an organization (organizational backing implies sustained resources and a succession path).

Revisiting the xz signals: in that incident, the attacker was able to infiltrate over a long period and eventually gain maintainer privileges precisely because the original maintainer was working alone, exhausted, and badly in need of help. In hindsight, “solo maintenance + prolonged strain + a suddenly enthusiastic new contributor” formed a complete risk pattern — and every one of those signals was publicly visible before the incident.

### 3. Responsiveness

The most direct evidence that a community is still alive is how it responds to external input.

• What is the median issue response time? How many issues were never answered at all?
• How long from PR submission to review? How many PRs sit indefinitely?
• Is there a dedicated security reporting channel (security.txt, SECURITY.md)? What does the historical security response time look like?

That last point matters especially for security review: a project with no security response process cannot give you any certainty when a vulnerability appears, however good its code quality.

### 4. Funding and governance structure

Softer indicators, but they determine long-term sustainability.

• Is the project hosted by a foundation (Linux Foundation, Apache, CNCF) or backed by a company?
• Are there governance documents, a decision process, contributor agreements?
• Is there a funding source — sponsorship, a commercial product, core maintainers employed to work on it?

A personal side project is not disqualifying; plenty of excellent foundational libraries started exactly that way. But it means the risk profile differs, and that difference belongs in the intake record rather than being ignored by default.

Where the data already exists

You do not need to build this assessment from scratch:

• OpenSSF Scorecard produces automated scores covering code review, branch protection, dependency updates, CI security testing and more — usable directly as a baseline reference.
• Repository metadata (contributors, commit history, release timeline) yields most of these metrics on its own.
• Security response history can be reconstructed from CVE records: for past vulnerabilities in this project, how long from disclosure to fix.

Worth flagging: star count is the least reliable indicator. It reflects historical popularity rather than current health, and plenty of high-star projects stopped being maintained long ago.

Embedding it in intake review

Health assessment becomes real when it is an explicit step in the adoption decision, with thresholds that vary by how critical the dependency is:

Core dependencies (in production, hard to replace) — demand more: active maintenance, bus factor above 1, a security response channel. If those are not met, require an architectural isolation plan or evaluate alternatives.

General dependencies — record the health score, include it in periodic review, and plan migration early when decline signals appear.

Development tooling — thresholds can be looser, but still register them, because the build environment is attack surface too.

Equally important is continuous monitoring: health changes. A project that was active at adoption may be abandoned two years later. Folding health into periodic dependency review — quarterly, say — is far more useful than checking once at intake.

A more fundamental view

Assessing community health is, at bottom, assessing a long-term relationship. You are not merely using some code; you are entrusting part of your own reliability to a group of people you have never met.

This is not a reason to avoid open source — modern software cannot exist without it. It is a reason to put “who maintains this, and for how long” on equal footing with “does it do what we need.”

---

Further reading: Building an open source intake policy · Managing EOL open source components · Project health entries in our Open Source & SBOM Guide