What Has Changed in 10 Years (2015–2025) - A data-driven view of how incubation actually works today

This guide summarises ten years of Incubator data (2015–2025) covering:

The goal is not to define rules, but to replace anecdotes with observable patterns so podlings and mentors can interpret their own progress in context.

1. Structural Change: A Smaller Incubator

One of the largest changes over the decade has nothing to do with processes or behaviour, it's that the Incubator itself is smaller.

Intake has also declined:

This structural shift must be accounted for in every interpretation of the data. A smaller Incubator naturally produces fewer messages, fewer threads, and fewer total graduations.

2. Time in Incubation: Graduation, Retirement, and Cohort Age

2.1 Graduation timing

The long-term pattern:

Thus, incubation appears faster today partly because:

Both effects matter.

2.2 Cohort-age effects

Recent cohorts (2019–2024) have not had time to accumulate:

Retirement typically happens 40–60 months after entry. Most modern cohorts are too young to fully reflect their distribution.

2.3 Graduation vs retirement probability

For fully matured cohorts (2015–2020 entries):

This has remained stable. There is no evidence that the Incubator has become “easier” or “harder”.

3. How Podling Population Affects Speed

Across 2015–2025, podling population strongly correlates with graduation speed:

In other words:

System size influences incubation speed. - When the Incubator is smaller, everything moves faster.

This structural factor must be considered when comparing eras.

4. Release Behaviour: Earlier, More Consistent, More Successful

4.1 Release health

Over the decade:

These improvements reflect real behavioural change.

4.2 Time to first release

Across all podlings:

By cohort:

More podlings attempt releases earlier.

5. The First Release Effect

The first release is the strongest predictor of long-term success.

Podlings that release early:

Podlings that delay their first release:

Those that release early almost always graduate.

6. Community Growth: First Governance Addition (Committer or PPMC)

To measure real community growth independent of bootstrap activity. (Note that all additions in the first 60 days of incubation are excluded.)
This section uses the earliest of:

This produces one consistent signal across all cohorts, avoiding historical differences in how podlings recorded roles.

6.1 Decade-wide pattern (2015–2024)

Across all podlings entering between 2015 and 2024:

The wide range reflects both fast-moving communities and long-delayed cases, especially among older cohorts.

6.2 Early cohorts (2015–2018)

For podlings entering 2015–2018:

These cohorts show slower and more variable early growth.

6.3 Modern cohorts (2019–2024)

For podlings entering 2019–2024:

These cohorts show faster, more consistent early growth with far fewer long delays.

6.4 Overall trend

Across the decade:

This earlier, more consistent signal aligns with other decade-long changes, such as earlier first releases, fewer stalled podlings, and more predictable progression.

7. Governance Formation and Activity

Graduating podlings show:

Retiring podlings:

These behavioural patterns are consistent across the decade.

8. Podling-Normalised Governance Load on incubator-general

Thread-level data from incubator-general shows a drop in raw message volume and total threads, but this closely matches the decline in podling population.

When adjusted for podlings:

Interpretation:

9. The Two Eras of Incubation

9.1 The Heavy Era (2014–2019)

9.2 The Fast Era (2021–2025)

Some improvements are structural (fewer podlings), but many are behavioural.

10. Early Warning Signs for Mentors

Across all data sets, the strongest practical indicators of trouble are:

Podlings with these patterns overwhelmingly resemble historical retirees.

Podlings with the opposite patterns overwhelmingly graduate.

10.1 Governance Normalised by Podling Count

Raw dev@ governance traffic rises sharply in 2016–2020 and then falls after 2021. However, this is almost entirely explained by changes in the number of active podlings. When we normalise by podling count, a different picture emerges.

Over the decade, podlings have consistently generated roughly 25–37 dev@ governance threads per year. The overall level of dev@ governance per podling has been remarkably stable, and there is no evidence of a long-term decline in oversight or activity.

Class-level behaviour shows clearer trends:

2021 shows a temporary dip in all classes, but activity rebounds immediately in 2022 and remains stable thereafter.

Overall, after adjusting for podling population, dev@ governance activity per podling is steady across the decade, with a shift toward more focused, vote-driven activity and fewer overhead or crisis-driven governance discussions.

10.2 Graduation and Retirement by Cohort

When we group podlings by the year they entered the Incubator, a clear pattern emerges. Older cohorts (2015–2017) show the full lifecycle: a mixture of graduates and retirees, with retirement rates typically around 20–40%, matching the long-term historical average.

Cohorts from 2018 onward show far fewer retirements, but this is a structural effect of cohort age rather than a behavioural change. Historically, retirement occurs 40–60 months after entry, while graduation usually occurs within 12–24 months. As a result:

This means that recent cohorts (2021–2024) have had enough time to generate graduates but not enough time to generate their retirements. The apparent drop in retirements and the apparently higher graduation rates of modern cohorts are both explained entirely by cohort age.

There is no evidence that modern podlings retire less often; we have not reached the point in the cohort lifecycle when retirements commonly occur.

The one real change visible in the cohort data is the shortened time to graduation. This reflects both structural factors (a smaller Incubator with fewer bottlenecks) and behavioural improvements (earlier releases, faster community growth, and clearer guidance).

In short:

11. Summary: What Has Actually Changed

Compared with a decade ago, the Incubator today is:

Structural improvements

Behavioural improvements

Cohort-age effects

And the key operational lesson:

First release + first committer + first PPMC growth tell you more about a podling’s future than any single numeric threshold.

12. Cohort Trends in Committer and PPMC Growth (2015–2025)

Committer and PPMC additions are the clearest indicators of real community growth. Across the decade, these signals show strong and consistent patterns, and the differences between cohorts help explain why modern podlings progress more predictably than earlier ones.

To avoid bootstrap noise, all measurements exclude additions made within the first 60 days of incubation.

12.1 Long-term shift: earlier and more consistent growth

Across the full dataset, we observe a clear shift:

Modern podlings are faster, more consistent, and less variable in their early growth.

12.2 Cohort-level patterns: what changed when

Breaking down by cohort shows the shift clearly:

2015–2017 cohorts (Heavy Era):

2018–2020 cohorts (Transition):

2021–2024 cohorts (Fast Era):

This marks the most consistent period of podling behaviour in the Incubator’s history.

12.3 Graduation vs retirement: growth trajectories diverge early

The difference between graduates and retirees is visible long before the outcome.

Graduating podlings:

Retiring podlings:

The divergence happens within the first 6–12 months, not at the end.

12.4 Earlier releases → earlier people

Cohort-level analysis reinforces the “first release effect”:

Podlings that make early releases reliably add new people sooner.

The behavioural chain observed across cohorts: First release → new contributors → first committer → first PPMC → stable governance → graduation

This pattern strengthened from 2019 onward and became dominant by 2021. Podlings delaying their first release often never add anyone.

12.5 Reduced variance: modern cohorts behave more predictably

A decade ago, podlings varied widely:

This unpredictability made mentor judgment and IPMC oversight more difficult.

Modern cohorts show tight clustering around the healthy pattern:

This reduced variance is one of the clearest improvements in incubation.

12.6 Structural vs behavioural components

The shift is partly structural and partly behavioural:

Structural:

Behavioural:

Both forces produce earlier growth and more consistent outcomes.

12.7 Implications for mentors

Cohort-level patterns sharpen mentor judgement:

Mentors no longer need to rely on intuition as cohort comparisons provide context.

12.8 Summary of cohort trends

Across 2015–2025:

Cohort-level growth trends show that the Incubator has become faster, more predictable, and significantly healthier over the past decade.

13. Technical Release Issue Improvements (2015–2025)

One of the most striking decade-long improvements is the sharp decline in technical issues found during release votes. This pattern is highly consistent across podlings, release attempts, and cohorts. The change is both quantitative (fewer issues) and qualitative (different kinds of issues).

Even though the number of release votes per podling has increased, the number of technical blockers has decreased. This is one of the clearest signs of improved podling behaviour and clearer expectations.

13.1 What counted as a “technical issue”?

From release-vote subject lines, mentor/IPMC commentary, and archived review notes, the typical technical issues included:

These issues formed the bulk of “-1 (binding)” reviews in the mid-2010s.

13.2 Long-term decline in technical failures

Across 2015–2025, clear trends emerge:

2015–2018 (Heavy Era):

2019–2021 (Transition):

2022–2025 (Fast Era):

The decline is not subtle, it is clear, consistent, and significant.

13.3 Why technical issues declined

Multiple systemic improvements contributed:

Better documentation

Increased mentor familiarity

Better podling behaviour

Fewer special-case projects

Technical issues declined both because podlings are better prepared and because mentors/IPMC reviewers are more consistent.

13.4 Re-votes, retries, and remediation

Technical retries now:

This is a dramatic contrast to the mid-2010s, when multi-cycle re-votes were common.

Modern podlings not only fail less often but they also recover faster.

13.5 Relationship with earlier releases

This pattern is deeply tied to the “first release effect”:

The rise in release-vote volume per podling corresponds directly with:

13.6 Summary of technical release improvements

Across the decade:

Taken together, technical improvements in release practices are among the strongest indicators that incubation today is faster and more predictable than it was a decade ago.

14. Cohort-Age Effects: Numerical Examples

Many apparent improvements in the last 3–4 years can only be understood when we account for cohort age. The simple fact is that recent podlings have not been in incubation long enough to produce long-tail graduates or long-tail retirees.

This section gives hard numerical examples to clarify the effect.

14.1 Retirement typically occurs 40–60 months after entry

From 2015–2020 cohort data:

This means:

Any cohort younger than ~3.5–5 years cannot possibly show its true retirement rate.

14.2 Recent cohorts have not yet matured

As of 2025:

These cohorts are far younger than the retirement window.

Thus:

Any apparent reduction in retirements is therefore a cohort-age artifact, not a behavioural change.

14.3 Time-to-graduation also depends on cohort age

Graduation tends to cluster much earlier than retirement:

This explains why:

The two signals operate on different clocks.

14.4 Example: comparing a mature cohort vs a young one

2016 cohort (mature):

2023 cohort (young):

The difference is time, not behaviour.

14.5 Numerical illustration: why “zero retirements since 2021” is not meaningful

This statement is superficially true but misleading:

Thus:

Of course recent cohorts show no retirements as they are too young.

A “missing” retirement signal for these cohorts is an expected outcome, not evidence of improved survival rates.

14.6 Why cohort-age matters for interpretation

Without adjusting for cohort age, the following misconceptions appear:

When corrected:

14.7 Summary of cohort-age effects

When interpreted with cohort age in mind, the decade shows consistency in outcomes, with the only real shifts being in speed, behaviour, and structural load, not in standards.

15. Confirming Stable Oversight Across Both dev@ and general@ (2015–2025)

After normalising all metrics by podling count, a single conclusion is unavoidable: ASF oversight per podling has remained stable throughout the decade.

This finding holds across both communication layers used during incubation, the podling’s own dev@ list (local governance) and the Incubator’s general@ list (ASF-level oversight).

Because raw message volumes track podlings numbers almost perfectly, only normalised analysis reveals the actual pattern.

15.1 dev@ governance is stable once normalised

Across ten years, podlings generate:

This range is consistent across:

Even when raw dev@ traffic rises or falls with podling population, the governance footprint per podling stays within the same narrow band.

This indicates:

15.2 general@ governance is also stable once normalised

Normalised incubator-general activity remains:

Again, this pattern holds across all eras.

Raw counts decline in later years simply because there are fewer podlings. Normalising removes that structural bias and reveals a steady oversight workload.

This confirms:

15.3 The mix of governance changed, but the amount did not

Across both lists:

This indicates:

The change is in quality, not quantity.

15.4 Oversight across eras: Heavy → Transition → Fast

Across the decade:

Heavy Era (2015–2019)

Transition (2020–2021)

Fast Era (2022–2025)

Through all of this:

The number of governance interactions per podling remained stable.

15.5 Why this matters for interpreting the decade

A common misinterpretation is:

Normalised data shows the opposite:

Incubator oversight is not declining, it is becoming more aligned with ASF norms.

15.6 Oversight quality increased even though oversight quantity remained stable

Additional indicators confirm this:

The quantity of oversight per podling stayed the same. The effectiveness of oversight increased.

15.7 Summary: oversight has not declined the system improved around it

Across both dev@ and general@:

Quieter raw traffic reflects fewer podlings and fewer problems, not disengagement or relaxed governance.

The Incubator today applies the same level of oversight per podling, but podlings progress through incubation more quickly, make releases earlier and more often, and show fewer long-running issues, resulting in a more predictable overall process.