MaintainerPulse

temporal prediction of dependency maintenance risk

requests-mock jamielennox/requests-mock ▲ high risk

82%
no release in the next 12 months
54%
new issues will go unanswered (30 days)
100%
maintainer activity collapse within 12 months
13%
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
010202023-092024-052025-012026-03

What drives this score

TreeSHAP contributions; raises risk / lowers risk
days since last release+package age (months)-releases, last 12mo+stars, all time+

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

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
responses A utility library for mocking out the `requests` Python library. 0.74 1 23%
pytest-mock Thin-wrapper around the mock package for easier use with pytest 0.57 4 29%
types-requests Typing stubs for requests 0.56 4 2%
scanapi Automated Testing and Documentation for your REST API 0.53 0 9%
moto A library that allows you to easily mock out tests based on AWS infrastructure 0.51 1 1%

Signals at the latest snapshot

days since last release 886 releases, last 12mo 0
people pushing, last 12mo 0 bus factor (top pusher share) -
issues opened, last 12mo 0 30-day response rate no issues
stars accumulated 475 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).