MaintainerPulse

temporal prediction of dependency maintenance risk

python-utils WoLpH/python-utils ✓ low risk

6%
no release in the next 12 months
20%
new issues will go unanswered (30 days)
100%
maintainer activity collapse within 12 months
51%
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

What drives this score

TreeSHAP contributions; raises risk / lowers risk
releases, all time-days since last release-months since any activity-package age (months)-

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

In plain words

Signals at the latest snapshot

days since last release 2 releases, last 12mo 2
people pushing, last 12mo 0 bus factor (top pusher share) -
issues opened, last 12mo 0 30-day response rate no issues
stars accumulated 64 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).