atdd

v3.103.1 safe
3.0
Low Risk

ATDD Platform - Acceptance Test Driven Development toolkit

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk across all categories with no network calls, shell risks likely benign, no obfuscation or credential harvesting detected. Metadata suggests low maintenance effort but does not indicate malicious intent.

  • Low network and obfuscation risk
  • No evidence of credential harvesting
  • Metadata suggests low maintenance effort
Per-check LLM notes
  • Network: No network calls detected, which is normal and not indicative of malicious activity.
  • Shell: Shell execution patterns are likely for version control operations and don't indicate malicious intent, but could be used to perform actions on the system.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low effort and lack of maintainer history, but there are no clear red flags indicating malicious intent.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (19174 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

  • 675 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in afokapu/atdd
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • gin/master"): probe = subprocess.run( ["git", "rev-parse", "--verify", candidate],
  • ain" try: base = subprocess.run( ["git", "merge-base", "HEAD", base_ref],
  • stdout.strip() diff = subprocess.run( ["git", "diff", f"{merge_base}..HEAD", "--name-
  • {} try: result = subprocess.run( [ "gh", "issue", "list",
  • .exists(): return subprocess.run( ["git", "worktree", "add", str(worktree_path), bran
  • th) -> None: try: subprocess.run( ["git", "worktree", "remove", "--force", str(wo
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository afokapu/atdd appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with atdd 
Your task is to develop a simple yet powerful utility application that leverages the 'atdd' package for acceptance test-driven development (ATDD). This application will serve as a demonstration of how ATDD can streamline software development by ensuring that the software meets the needs of its end-users from the very beginning of the development process.

The application you'll create will be called 'FeatureSpecRunner'. It will allow developers to define their application's features through a set of specifications (specs), which describe how the application should behave in various scenarios. These specs will then be used to generate acceptance tests that can be run against the application to ensure it behaves as expected.

Here are the key features your 'FeatureSpecRunner' app should have:
1. **Spec Definition**: Users should be able to define feature specs using a simple, human-readable format. Each spec will include a description of the feature, preconditions, actions, and expected outcomes.
2. **Test Generation**: Based on the defined specs, the app should automatically generate acceptance tests in a format that can be executed by common testing frameworks like pytest.
3. **Execution and Reporting**: The app should provide functionality to execute these generated tests and report back on the results. Reports should clearly indicate which tests passed and which failed, along with any relevant details.
4. **Integration Testing**: Include a feature that allows users to integrate their feature specs directly into their continuous integration (CI) pipeline, so that every code commit triggers an automated test run based on the defined specs.
5. **User Interface**: While not mandatory, consider adding a basic command-line interface (CLI) or a simple web-based UI for defining and managing feature specs.

To achieve these goals, you'll utilize the 'atdd' package, which provides tools for defining, generating, and executing acceptance tests in the context of ATDD. Your implementation should demonstrate a deep understanding of how ATDD can enhance the quality and user satisfaction of software products.

Your final deliverables should include:
- A fully functional 'FeatureSpecRunner' application.
- Documentation on how to use the app, including examples of feature specs.
- A brief explanation of how the 'atdd' package was integrated into your solution.

This project aims to showcase the power of ATDD in making software development more efficient and user-centric.

💬 Discussion Feed

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