MaintainerPulse
temporal prediction of dependency maintenance riskpytest-mock pytest-dev/pytest-mock ● elevated
52%
no release in the next 12 months
28%
new issues will go unanswered (30 days)
38%
maintainer activity collapse within 12 months
70%
chance of a release within 12 months (survival model)
Model risk over time
calibrated P(no release next 12mo) at each historical monthly snapshot
Repository activity, last 36 months
pushes
issues
PyPI release
What drives this score
TreeSHAP contributions; raises risk /
lowers risk
Survival curve
P(still no release) m months ahead, discrete-time hazard model
What if? — poke the model
drag a signal and the deployed models rescore this package live (implied signals move together — two years without a release also zeroes "releases, last 12mo"). The needle tracks the activity-collapse model, a logistic regression: monotone by construction, so it responds smoothly to counterfactuals where the tree models step.
maintainer activity collapse, 12mo
Maintained alternatives
similar packages with low predicted risk; ranked by summary similarity
blended with shared-dependents overlap, validated against known migrations
| package | what it is | similarity | shared dependents | its 12mo risk |
|---|---|---|---|---|
| pytest | pytest: simple powerful testing with Python | 0.76 | 66 | 2% |
| pytest-rerunfailures | pytest plugin to re-run tests to eliminate flaky failures | 0.67 | 8 | 10% |
| zope.pytestlayer | Integration of zope.testrunner-style test layers into pytest framework | 0.67 | 0 | 36% |
| pytest-run-parallel | A simple pytest plugin to run tests concurrently | 0.65 | 0 | 5% |
| pytest-xdist | pytest xdist plugin for distributed testing, most importantly across multiple CPUs | 0.59 | 16 | 13% |
Signals at the latest snapshot
| days since last release | 350 | releases, last 12mo | 2 |
| people pushing, last 12mo | 2 | bus factor (top pusher share) | 56% |
| issues opened, last 12mo | 1 | 30-day response rate | 100% |
| stars accumulated | 1886 | open vulns without fix | 0 |
Probabilities are isotonic-calibrated on a held-out validation year and evaluated on future snapshots the models never saw (PR-AUC 0.894, precision@50 = 1.00 on the headline target).