MaintainerPulse

temporal prediction of dependency maintenance risk

starkbank-ecdsa starkbank/ecdsa-python ● elevated

43%
no release in the next 12 months
54%
new issues will go unanswered (30 days)
100%
maintainer activity collapse within 12 months
26%
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%2019-032021-042023-052025-06high risk

Repository activity, last 36 months

pushes issues PyPI release
05102023-092024-042024-102026-05

What drives this score

TreeSHAP contributions; raises risk / lowers risk
releases, all time-days since last release-stars, all time+new stars, 12mo+

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

Maintained alternatives

similar packages with low predicted risk; ranked by summary similarity blended with shared-dependents overlap, validated against known migrations
packagewhat it issimilarity shared dependentsits 12mo risk
ecdsa ECDSA cryptographic signature library (pure python) 0.77 0 13%
anycrc The fastest general Python CRC Library 0.64 0 21%
slh-dsa Pure Python implementation of the SLH-DSA algorithm (based on FIPS 205). 0.60 0 29%
epicscorelibs The EPICS Core libraries for use by python modules 0.58 0 29%
tibs A sleek Python library for binary data. 0.56 0 5%

Signals at the latest snapshot

days since last release 131 releases, last 12mo 2
people pushing, last 12mo 3 bus factor (top pusher share) 71%
issues opened, last 12mo 0 30-day response rate no issues
stars accumulated 151 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).