MaintainerPulse
temporal prediction of dependency maintenance risksambanova sambanova/sambanova-python ✓ low risk
6%
no release in the next 12 months
54%
new issues will go unanswered (30 days)
52%
maintainer activity collapse within 12 months
98%
chance of a release within 12 months (survival model)
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
- bus factor: one person makes 88% of pushes
- healthy cadence: 20 releases in the last year
Signals at the latest snapshot
| days since last release | 5 | releases, last 12mo | 20 |
| people pushing, last 12mo | 2 | bus factor (top pusher share) | 88% |
| issues opened, last 12mo | 0 | 30-day response rate | no issues |
| stars accumulated | 0 | 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).