MaintainerPulse

temporal prediction of dependency maintenance risk

robotframework-appiumlibrary serhatbolsu/robotframework-appiumlibrary ✓ low risk

36%
no release in the next 12 months
46%
new issues will go unanswered (30 days)
100%
maintainer activity collapse within 12 months
32%
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
025502023-092024-072025-052026-08

What drives this score

TreeSHAP contributions; raises risk / lowers risk
releases, all time-stars, all time+events, all time+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 260 releases, last 12mo 2
people pushing, last 12mo 1 bus factor (top pusher share) 100%
issues opened, last 12mo 0 30-day response rate no issues
stars accumulated 352 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).