MaintainerPulse
temporal prediction of dependency maintenance riskjupyterlab-chat jupyterlab/jupyter-chat ✓ low risk
2%
no release in the next 12 months
28%
new issues will go unanswered (30 days)
29%
maintainer activity collapse within 12 months
100%
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
In plain words
- healthy cadence: 38 releases in the last year
Signals at the latest snapshot
| days since last release | 4 | releases, last 12mo | 38 |
| people pushing, last 12mo | 4 | bus factor (top pusher share) | 75% |
| issues opened, last 12mo | 10 | 30-day response rate | 40% |
| stars accumulated | 29 | 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).