autofaker

v2.0.24 suspicious
4.0
Medium Risk

Python library designed to minimize the setup/arrange phase of your unit tests

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell, obfuscation, and credential handling. However, the metadata risk due to the maintainer's new or inactive account and lack of proper author identification raises some concern.

  • Metadata risk due to new or inactive maintainer account and missing author information.
  • Otherwise, all other risk factors are at minimal levels.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a utility package like autofaker that does not require internet access.
  • Shell: No shell execution patterns detected, which aligns with the expected behavior of a utility package focused on generating fake data.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 46 test file(s) found

  • 46 test file(s) detected (e.g. test_decorator_anonymous_builtins.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (11651 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

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

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in christianhelle/autofaker
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: outlook.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository christianhelle/autofaker appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 autofaker 
Create a simple and efficient data generation tool using the Python package 'autofaker'. This tool will serve as a utility for developers and testers who need to quickly generate mock data for testing purposes. The application should have the following functionalities:

1. **Data Generation**: The tool should be able to generate various types of data including strings, integers, floats, and lists.
2. **Custom Data Models**: Users should be able to define their own custom data models using classes or dictionaries, which the tool will then use to generate instances of these models.
3. **Configuration Options**: Allow users to configure the generation process through command-line arguments or a configuration file, specifying details such as number of records to generate, seed for reproducibility, and specific attributes to focus on.
4. **Output Formats**: Provide options to output the generated data in different formats such as CSV, JSON, or directly to a database.
5. **Integration with Unit Tests**: Showcase how the generated data can be directly integrated into unit tests for a sample application, demonstrating the reduction in setup time for tests.

The core of the application will leverage the 'autofaker' package to handle the data generation logic, minimizing the amount of boilerplate code needed. The goal is to create a user-friendly tool that significantly speeds up the process of generating test data, thereby enhancing the efficiency of software development and testing cycles.

💬 Discussion Feed

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