approvaltests

v18.0.5 safe
3.0
Low Risk

Assertion/verification library to aid testing

🤖 AI Analysis

Final verdict: SAFE

The package approvaltests v18.0.5 has been assessed and found to have low risks associated with it. It does make network calls to fetch raw content from GitHub, but there are no indications of malicious intent or other security issues.

  • Network calls to GitHub for raw content.
  • No shell execution, obfuscation, or credential harvesting detected.
Per-check LLM notes
  • Network: The network call appears to be fetching raw content from GitHub which is not inherently malicious but could be used for nefarious purposes if misused.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected in the package.
  • Credentials: No credential harvesting patterns detected in the package.
  • Metadata: Low risk with non-secure links but no suspicious flags otherwise.

📦 Package Quality Overall: Low (4.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (13171 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 190 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 100 commits in approvals/ApprovalTests.Python
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • False response = requests.get( f"https://raw.githubusercontent.com/approva
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://approvaltests.com/
  • Non-HTTPS external link: http://www.approvaltests.com
✓ Git Repository History

Repository approvals/ApprovalTests.Python appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "ApprovalTests Contributors" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with approvaltests 
Create a mini-application called 'ApprovalChecker' that utilizes the Python package 'approvaltests' to verify user-generated content against approved samples. This application will serve as a tool for content moderation, ensuring that user inputs match expected standards without manual intervention.

### Step-by-Step Guide:
1. **Setup Project**: Initialize a new Python project and install the 'approvaltests' package.
2. **Define User Inputs**: Create a function that accepts various types of user inputs (text, images, etc.).
3. **Approval Process**: Implement a mechanism to approve/reject user inputs manually. Store these approved samples in a designated directory within your project.
4. **Verification Logic**: Use 'approvaltests' to compare future user inputs against the approved samples. If the input matches an approved sample, it passes; otherwise, it fails.
5. **Feedback Loop**: For failed tests, provide a clear feedback message indicating why the input did not pass verification.
6. **User Interface**: Develop a simple command-line interface for users to submit their inputs and view the verification results.
7. **Testing**: Write comprehensive tests using 'approvaltests' to ensure the verification logic works as intended.
8. **Documentation**: Document the setup process, usage instructions, and any special considerations for maintaining the application.

### Suggested Features:
- **Multi-Type Input Support**: Allow the application to handle different types of inputs (text, images).
- **Customizable Approval Directory**: Enable users to specify where approved samples are stored.
- **Detailed Error Messages**: Provide specific feedback on why an input failed verification.
- **Integration with External Services**: Consider integrating with external services like email notifications for failed verifications.
- **Version Control for Approvals**: Keep track of different versions of approved samples.

### Utilizing 'approvaltests':
- **Approval Verification**: Use 'approvaltests.ApprovalTest.verify()' to compare user inputs against approved samples.
- **Approval Setup**: Use 'approvaltests.ApprovalTest.approve(actual)' during the initial setup phase to store the first set of approved samples.
- **Error Handling**: Leverage 'approvaltests.ApprovalTest.verify(actual, file_name)' to generate error messages when inputs fail verification.

This project aims to demonstrate the power of 'approvaltests' in automating the verification process and maintaining high-quality content standards.