archunitpython

v1.1.1 safe
4.0
Medium Risk

Architecture testing library for Python projects. Enforce dependency rules, detect cycles, validate metrics.

🤖 AI Analysis

Final verdict: SAFE

The package does not pose significant risks based on the provided analysis notes. While there is some obfuscation through dynamic imports and concerns over incomplete metadata, these factors alone do not strongly indicate malicious activity.

  • No network or shell risks detected.
  • Incomplete author information and potential use of dynamic imports for obfuscation.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell executions detected, indicating no immediate risk of command execution.
  • Obfuscation: The use of dynamic imports may indicate an attempt to hide code execution paths, but it could also be used for legitimate purposes like plugin systems.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The author information is incomplete and the account seems new or inactive, which raises some concerns but not enough to strongly indicate malicious intent.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_setup.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://lukasniessen.github.io/ArchUnitPython/
  • Detailed PyPI description (18056 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • Type checker (mypy / pyright / pytype) referenced in project
  • 215 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 52 commits in LukasNiessen/ArchUnitPython
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • DYNAMIC_IMPORT = "dynamic" # __import__('foo') / importlib.import_module() TYPE_IMPORT = "type" # ins
✓ 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

All external links appear legitimate

✓ Git Repository History

Repository LukasNiessen/ArchUnitPython 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 archunitpython 
Create a Python-based application named 'PyArchChecker' which will serve as a tool to analyze and validate the architecture of Python projects using the 'archunitpython' package. This application should provide a user-friendly interface where users can input paths to their Python projects, and it will output a detailed report on the architectural health of those projects. Here are the core functionalities you need to implement:

1. **Dependency Analysis**: Allow users to specify certain modules or packages within their projects and check for unwanted dependencies between them. For example, ensure that no business logic code depends on infrastructure code.
2. **Cycle Detection**: Automatically scan the project for circular dependencies between modules and packages. Highlight these cycles in the report so developers can address them.
3. **Metric Validation**: Define and apply various architectural metrics such as 'Depth of Inheritance Tree', 'Number of Children', etc., to assess the complexity and maintainability of the codebase. Provide recommendations based on these metrics.
4. **Rule Enforcement**: Users should be able to define custom rules regarding class structures, method counts, package sizes, etc. The app will then check if these rules are being followed throughout the project.
5. **Report Generation**: After performing the analysis, generate a comprehensive HTML report summarizing all findings, including any violations of rules, detected cycles, and metric scores. This report should also include suggestions for improvement.

To utilize 'archunitpython', you'll first need to install it via pip. Then, use its functions to parse the project structure, perform the necessary checks, and validate against defined rules. Make sure to handle exceptions gracefully and provide meaningful error messages to guide users through troubleshooting common issues.

💬 Discussion Feed

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