Is Asynctest Safe?
Asynctest — Nerq Trust Score 72.0/100 (B grade). Score based on 2 independent trust signals. Last analyzed: 2026-04-12
Asynctest is a Python package with a Nerq Trust Score of 72.0/100 (B), based on 3 independent data dimensions. Last analyzed: 2026-04-12 Security: 90/100. Popularity: 90/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-04-12. Machine-readable data (JSON).
Is Asynctest safe?
Trust Score Breakdown — Asynctest has a Nerq Trust Score of 72.0/100 (B). Measured across 2 independent trust signals (as of 2026-04-12).
What is Asynctest's trust score?
Asynctest has a Nerq Trust Score of 72.0/100, earning a B grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Asynctest?
Asynctest's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Asynctest and who maintains it?
| Author | Martin Richard |
| Category | Python Packages |
| Source | N/A |
Similar Pypi by Trust Score
Safety Guide: Asynctest
What is Asynctest?
Asynctest is a Python package — Enhance the standard unittest package with features for testing asyncio libraries.
How to Verify Safety
Run pip audit or safety check. Review on PyPI for download stats.
You can also check the trust score via API: GET /v1/preflight?target=asynctest
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Asynctest has a Nerq Trust Score of 72/100 (B). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Asynctest has a Trust Score of 72/100 (B).
- The score is a measured composite — it is not a suitability judgment. Evaluate the individual signals against your own requirements.
- Query the current measured values via the Nerq API.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Maintenance | 71/100 |
| Popularity | 90/100 |
| Quality | 65/100 |
| Community | 35/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Asynctest collect?
Asynctest is a Python package maintained by Martin Richard. It receives approximately 2,849,946 weekly downloads. Licensed under Apache 2.
As a development package, Asynctest does not directly collect end-user personal data. However, applications built with it may collect data depending on implementation. Privacy score: 80/100.
Review the package's dependencies for potential supply chain risks. Run your package manager's audit command regularly.
Full analysis: Asynctest Privacy Report · Privacy review
Is Asynctest secure?
Security score: 90/100. Asynctest has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
Licensed under Apache 2, allowing code inspection. Open-source packages allow independent security review of the source code.
Run your package manager's audit command (`npm audit`, `pip audit`, `cargo audit`) to check for known vulnerabilities in your dependency tree.
Full analysis: Asynctest Security Report
How we calculated this score
Asynctest's trust score of 72.0/100 (B) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), maintenance (71/100), popularity (90/100), quality (65/100), community (35/100). Each dimension is weighted equally to produce the composite trust score.
Nerq analyzes over 7.5 million entities across 26 registries using the same methodology, enabling direct cross-entity comparison. Scores are updated continuously as new data becomes available.
Signals last measured on April 12, 2026. Data version: 1.0.
Full methodology documentation · Machine-readable data (JSON API)
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated measurements based on publicly available signals. They are not endorsements, verdicts, or guarantees of suitability. Always evaluate the signals against your own requirements.