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There is no single number that proves an open-source ecosystem is healthy. A defensible assessment starts with the decision you need to make, then combines comparable measures of community activity, contributions, sustainability, governance, and security over a defined period. CHAOSS provides implementation-agnostic metrics, models, and software for this work; OpenSSF Scorecard adds security-focused checks, but neither is a universal pass/fail rating.
This guide shows maintainers, researchers, funders, ecosystem stewards, and software adopters how to design the measurement, collect evidence, compare projects fairly, and avoid misleading conclusions.
What ecosystem health means
In this context, an ecosystem is the project and the people, organizations, dependencies, infrastructure, and processes around it. Health describes whether that system can continue producing and maintaining useful software for its intended users. It is broader than popularity, repository activity, download volume, or a security score.
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CHAOSS is a Linux Foundation project focused on metrics, models, and software for understanding community health. Its metrics committee covers community activity, contributions, health, and sustainability, while its charter supports implementation-agnostic measures and use-case-specific analysis. The charter was last updated January 14, 2026.
Start with the decision, not the dashboard
Write a one-sentence decision statement before selecting indicators. Different audiences need different evidence:
- Maintainer improvement: identify bottlenecks in reviews, issue handling, onboarding, or bus-factor risk.
- Dependency selection: judge whether a project is an acceptable fit for a particular product, deployment, and support horizon.
- Funder evaluation: understand whether funding is improving resilience, participation, or security practices.
- Ecosystem monitoring: observe trends across many projects without pretending that projects of different sizes and ages are interchangeable.
- Research: test a clearly defined hypothesis with reproducible data definitions and observation windows.
Record the decision, audience, scope, and review date in the measurement plan. A metric that helps a maintainer prioritize documentation may be irrelevant to a procurement decision. CHAOSS’s use-case orientation is a reason to choose measures deliberately rather than copy a generic scorecard.
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A practical minimum model has three dimensions. You can add dimensions when the decision requires them, but do not collapse the following into one composite number without a documented rationale.
| Dimension | What it can reveal | Example evidence to define | Interpretation caution |
|---|---|---|---|
| Community activity and contributions | Whether people participate, how participation is distributed, and how work moves through the project. | Contributor and reviewer participation, issue and pull-request flow, review latency, release activity, and onboarding signals. | Raw counts depend on project size, age, release cycle, and workflow. |
| Sustainability and governance | Whether the project has durable people, processes, resources, and decision-making structures. | Maintainer continuity, succession or backup arrangements, documented decision rules, release responsibilities, funding or support commitments, and concentration of critical work. | There is no universal CHAOSS threshold for a sustainable project; select evidence for the use case. |
| Security and dependency risk | Whether important security practices are present and whether dependencies create material exposure. | Relevant OpenSSF Scorecard checks, OpenSSF Best Practices criteria, dependency freshness signals such as Libyears, vulnerability response evidence, and deployment-specific likelihood and impact. | Security signals are contextual. Popularity does not prove good security practice. |
Security is essential but is only one part of ecosystem health. A project can score well on selected security checks while losing maintainers, or have an active community while exposing adopters to unacceptable deployment risk.
Write a measurement specification
Before collecting data, create a short specification that another analyst could follow. Include:
- Unit of analysis: define whether you are measuring a repository, a coordinated project with several repositories, a package, a release line, or an ecosystem of related projects.
- Population and exclusions: state which repositories, bots, mirrors, generated files, archived branches, and external services are included or excluded.
- Observation window: choose exact start and end dates. A rolling window can show current direction; a fixed window makes a comparison reproducible.
- Metric definition: specify the numerator, denominator, event timestamp, and treatment of reopened, transferred, duplicated, or abandoned work.
- Source and coverage: identify the API, export, repository history, issue tracker, security report, or survey used, plus any missing periods.
- Refresh and ownership: name who updates the measure, how often, and what happens when a data source changes.
- Decision rule: explain how evidence will affect an action. Avoid inventing a threshold merely because a dashboard needs a color.
Consistent definitions and time windows are methodological requirements for meaningful comparison. They are not a universal CHAOSS formula; choose them to fit the decision and document the choice.
Measure community activity and contributions
Activity measures describe participation and work flow, not value by themselves. Use several indicators together and examine direction, distribution, and context.
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Participation and concentration
Count distinct human contributors during the window, then examine how participation is distributed across code, documentation, issue triage, reviews, design, releases, and community support. Separate recurring participants from one-time drive-by contributions when the distinction matters. Identify whether critical areas depend on one person or one organization. A rising contributor count can coexist with a shrinking reviewer pool, so do not report only the headline total.
Work intake and completion
Track opened, closed, and unresolved issues or change requests, along with age bands for outstanding work. Review throughput, time from submission to first response, and time from accepted change to release can expose bottlenecks. Define how bots, duplicates, security embargoes, and discussions that never become actionable work are handled.
Review and collaboration quality
Measure participation in reviews, the number of independent reviewers for important changes, and whether review responsibility is concentrated. A high change count with little independent review may indicate delivery pressure rather than health. Conversely, a low change count with prompt, substantive review may reflect a stable release cycle.
Onboarding and retention signals
Look for repeat participation after a first contribution, documentation changes that reduce setup friction, and the time required for a newcomer to receive a useful response. These are diagnostic signals rather than universal standards. Explain exactly how you classify a first contribution and what data is unavailable.
Releases and maintenance rhythm
Record release dates, supported branches, and whether security or bug fixes reach users through a predictable process. Compare the observed rhythm with the project’s stated lifecycle. Do not assume that more releases are better: a stable library may intentionally release less often, while a rapidly changing tool may require frequent releases.
Assess sustainability and governance
Sustainability asks whether the project can keep operating when priorities, funding, employers, or individual maintainers change. Governance asks whether participants can understand how decisions are made and how disagreements are resolved.
People and succession
Document maintainers, release managers, security contacts, and backups where the project publishes them. Examine the concentration of review, release, and infrastructure privileges. A concentrated role is not automatically unhealthy, but it is a continuity risk if no handover path exists.
Decision clarity
Check whether contribution rules, code of conduct procedures, licensing information, roadmap decisions, and escalation paths are legible to outsiders. Note the difference between a process being documented and being followed; use issue or release evidence to test the latter.
Resources and incentives
Record disclosed funding, organizational support, grants, sponsorships, or paid maintenance commitments only when they are relevant and verifiable. Funding is an input, not proof of outcomes. Connect resources to the work they are intended to sustain, such as security response, infrastructure, documentation, or release engineering.
Organizational and geographic resilience
Where the data is available and appropriate to collect, examine whether critical knowledge is spread across people, employers, and regions. Treat identities carefully: employer changes, pseudonyms, shared accounts, and privacy choices can distort attribution. Report uncertainty instead of presenting inferred identity as fact.
Include security without turning it into a health score
CHAOSS’s practitioner guidance recommends starting with security trends and identifies OpenSSF Best Practices criteria and Libyears as useful starting points. It also emphasizes that risk depends on likelihood and impact in a particular deployment context. A vulnerable optional dependency in an isolated development tool does not carry the same consequence as a vulnerable library exposed in a production authentication path.
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OpenSSF Scorecard is an automated security-oriented tool. It assigns each check a score from 0 to 10. Checks address specific project practices and can support improvement work or dependency-risk assessment.
The project documentation is explicit: Scorecard is not intended to be a one-size-fits-all solution.
It also says, The checks themselves are heuristics; there are false positives and false negatives.
Treat every result as a prompt for verification. Read the individual check, inspect the evidence, and decide whether that behavior matters to your deployment.
Do not rely on the aggregate
An aggregate Scorecard result can hide materially different profiles. Two projects can have the same average while one lacks branch protection and the other has weak dependency updating. Report the component checks that affect your threat model, the scan date, the tool version if available, and any exceptions you verified manually.
Understand current coverage limits
Scorecard’s repository documentation says it scans more than 1.3 million repositories every week and distributes results through its REST API. The same documentation says its former public BigQuery dataset is no longer available. These operational details can change, so record the retrieval date and do not treat the scan population as the total universe of open-source projects.
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Combine project signals with deployment context
For each relevant dependency, write down the asset at risk, the plausible attack path, the likelihood, and the impact. Then map the project’s practices and known gaps to mitigations such as pinning versions, restricting permissions, monitoring advisories, isolating runtime access, or choosing a maintained release line. A project signal alone does not determine risk for every adopter.
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Collect and analyze the evidence step by step
- State the decision and scope. Name the audience, projects, repositories, release lines, and decision date.
- Build the metric dictionary. For every measure, write its definition, unit, exclusions, source, owner, and time window.
- Establish a baseline. Capture at least one complete period before interpreting change. Preserve raw extracts or reproducible queries where licensing and privacy rules permit.
- Validate identities and automation. Classify bots, mirrors, service accounts, and duplicate identities consistently. Record unresolved ambiguity.
- Calculate rates and distributions. Use rates or normalized measures when comparing projects of different sizes, but retain the underlying counts so readers can understand scale.
- Segment the result. Break activity down by role, repository, release line, organization, or work type when aggregation would hide a risk.
- Add qualitative evidence. Read governance documents, release notes, security advisories, and maintainer communications. Metrics should guide questions, not replace them.
- Review security checks individually. Run or retrieve relevant Scorecard checks, inspect their evidence, and note false-positive or false-negative possibilities.
- Interpret against lifecycle. Label projects as emerging, growing, mature, maintenance-focused, archived, or otherwise applicable; explain how that status changes interpretation.
- Record limitations and decide. List missing data, source changes, privacy constraints, and uncertainty before recommending an action.
Compare projects fairly
When assessing two or more projects, use the same definitions, observation windows, source coverage, and treatment of bots and repositories. Do not compare raw counts without accounting for project size, age, release cycle, and data coverage.
| Comparison axis | Questions to ask | Evidence to present |
|---|---|---|
| Community activity and contributions | Is participation growing, stable, or narrowing? Is important work reviewed by more than one person? | Trend and distribution of contributors, reviews, issue flow, response times, and release work. |
| Sustainability and governance | Can outsiders understand decisions? Is there backup for critical roles? Are resources aligned with obligations? | Published processes, maintainer continuity, role concentration, support commitments, and observed adherence. |
| Security practices and dependency risk | Which checks matter for the intended deployment, and what is the consequence of each gap? | Relevant Scorecard checks, Best Practices criteria, Libyears or other freshness signals, advisories, and likelihood-impact analysis. |
| Evidence quality | How complete, recent, and reproducible is the data? Which measures are heuristic? | Source, retrieval date, coverage gaps, tool version, definitions, and known uncertainty. |
Use a side-by-side narrative when the projects serve different roles. A young framework, a mature compiler, and a small plugin should not be ranked as if they had the same expected activity pattern.
Interpret signals instead of ranking blindly
Low activity
Investigate whether low activity reflects stability, a small effective team, a completed maintenance phase, or abandonment. Check open critical issues, release support, response behavior, and documented ownership before drawing a conclusion.
Rapid activity growth
Growth can indicate adoption, a coordinated migration, a security response, or a temporary campaign. Verify whether review capacity, release quality, and governance grew with it.
High contributor count
A high count may come from documentation drives, generated changes, or one-time participation. Examine recurring contributors, independent reviewers, and concentration of essential work.
High security score
A strong Scorecard profile means that the measured checks produced strong results; it does not certify the code, eliminate vulnerabilities, or establish overall ecosystem health. Confirm that the checks cover your actual threat model.
Missing or changing data
Mark unavailable observations as unavailable. Do not convert missing values to zero. If a provider changes an API, repository layout, or scoring heuristic, preserve the old definition and explain the break in the time series.
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CHAOSS maintains catalogs of metrics, models, and software intended to support community-health analysis. Its related listings include Metrix, OSS Compass, and Augur. These references establish relevance to the metrics work, not comparative performance, pricing, support guarantees, or a recommendation for one service.
Use a tool when it reduces collection or reproducibility work, but keep the metric dictionary independent of the tool. A dashboard can change labels, APIs, or scoring logic; your definitions and decision rationale should remain readable without it. For security, use Scorecard as one input and retain the individual check evidence rather than exporting only a composite number.
Create a report readers can audit
A useful report lets another person understand what was measured and why. Include:
- Decision and audience: the action the assessment supports.
- Scope: projects, repositories, versions, and exclusions.
- Definitions: metric formulas, identity treatment, and bot handling.
- Time window: exact dates, refresh cadence, and release context.
- Results: counts, rates, distributions, trends, and segmented views.
- Security detail: relevant Scorecard checks, scan date, tool limitations, and deployment-specific risk.
- Qualitative evidence: governance, release, and incident observations that explain the numbers.
- Limitations: missing data, privacy constraints, heuristic measures, and known breaks in comparability.
- Decision and follow-up: what will change, who owns it, and when the evidence will be reviewed.
Keep raw observations separate from interpretation. For example, report that the reviewer pool narrowed during the window, then explain whether that creates a continuity risk given the project’s documented backups and release process. This makes disagreement about the conclusion productive without confusing it with disagreement about the underlying data.
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A repeatable review cadence
Use a cadence that matches the decision. Maintainers may inspect operational signals weekly or per release, while funders and ecosystem stewards may review quarterly or semiannually. At each review:
- Confirm that source coverage and definitions have not changed.
- Update trend and distribution views.
- Investigate outliers and abrupt breaks before labeling them improvements or declines.
- Recheck the security practices that matter to the current deployment and release line.
- Ask maintainers or community representatives to explain material changes.
- Record the action, owner, and next review date.
Do not manufacture a trend when the window is too short or the project releases irregularly. State when the evidence is insufficient for a confident conclusion.
Common mistakes to avoid
- One-number health rankings: they erase trade-offs and hide which behavior produced the result.
- Popularity as a proxy for security: popular packages are not inherently more or less likely to follow good security practices.
- Raw-count comparisons: they favor larger or older projects and ignore lifecycle.
- Unqualified thresholds: a target copied from another project may be meaningless for yours.
- Ignoring non-code work: documentation, triage, mentoring, release engineering, and incident response sustain ecosystems.
- Attributing identity with certainty: privacy choices and shared accounts make contributor analysis imperfect.
- Treating heuristics as audits: Scorecard checks can produce false positives and false negatives.
- Hiding missing data: a blank, unavailable, or changed source must be disclosed rather than silently treated as zero.
- Confusing funding with health: resources matter only in relation to the work and outcomes they support.
The practical answer to “How do I measure the health of an open-source project?”
Define the decision, select multiple dimensions, and publish a reproducible measurement specification. Examine community activity and contribution distribution, sustainability and governance, and security and dependency risk. Compare only like-for-like windows and definitions. Interpret every signal in the project’s lifecycle and deployment context. Use OpenSSF Scorecard’s 0-to-10 checks to investigate specific security practices, not to declare a project healthy from an aggregate. Finally, report uncertainty, missing evidence, and the action the findings justify.
This approach produces a defensible picture without pretending that ecosystem health has a universal score. It also makes the assessment useful: maintainers can target improvements, adopters can understand dependency risk, funders can evaluate resilience, and ecosystem stewards can see where additional evidence or support is needed.
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