MaintainerPulse

temporal prediction of dependency maintenance risk

async-lru aio-libs/async-lru ✓ low risk

32%
no release in the next 12 months
61%
new issues will go unanswered (30 days)
52%
maintainer activity collapse within 12 months
55%
chance of a release within 12 months (survival model)

Model risk over time

calibrated P(no release next 12mo) at each historical monthly snapshot
0%50%100%2018-012020-072022-122025-06high risk

Repository activity, last 36 months

pushes issues PyPI release
025502023-092024-092025-082026-08

What drives this score

TreeSHAP contributions; raises risk / lowers risk
events, all time+releases, all time-stars, all time+days since last release-

Survival curve

P(still no release) m months ahead, discrete-time hazard model
0%50%100%036912

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

Signals at the latest snapshot

days since last release 166 releases, last 12mo 3
people pushing, last 12mo 2 bus factor (top pusher share) 77%
issues opened, last 12mo 1 30-day response rate 0%
stars accumulated 669 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).