agentic-testari

v0.1.4 suspicious
4.0
Medium Risk

Agentic Testari — autonomous QA testing under AIOS development discipline

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate metadata risk due to potential new or inactive maintainer activity and limited repository details, which raises concerns about its legitimacy and ongoing support.

  • Moderate metadata risk
  • Limited repository and author details
Per-check LLM notes
  • Network: The network call patterns suggest legitimate HTTP requests but warrant caution to ensure proper handling and validation of responses.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of potential new or inactive maintainer activity with limited repository and author details, raising some concerns.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • } async with httpx.AsyncClient(timeout=self._timeout) as client: response = aw
  • client: Optional injected httpx.Client (tests pass a mock). Returns: ``(True, "ok")`
  • nt if client is not None else httpx.Client(timeout=timeout) owns_client = client is None try:
  • self._client return httpx.Client( base_url=self._base_url, timeout=
✓ 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: agentictestari.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ 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 agentic-testari 
Develop a fully-functional mini-application called 'AutoTestSuite' that leverages the 'agentic-testari' package to automate quality assurance testing processes. This application will serve as a versatile tool for developers looking to streamline their testing workflows using AI-driven methodologies. Here’s a step-by-step guide on how to create this application:

1. **Project Setup**: Begin by setting up your Python environment. Ensure you have Python 3.8 or higher installed. Use pip to install the 'agentic-testari' package along with other necessary libraries such as pytest for unit testing.

2. **Application Architecture**: Design the architecture of AutoTestSuite. It should include modules for test case generation, execution, and result analysis. Consider incorporating a user-friendly interface that allows users to specify test scenarios and view results.

3. **Integration with agentic-testari**: Utilize the core functionalities of 'agentic-testari' to automate the creation and execution of test cases. Explore how its AI capabilities can enhance the testing process, such as predicting potential issues based on historical data or suggesting test cases that cover edge cases.

4. **Feature Implementation**:
   - **Dynamic Test Case Generation**: Implement a feature that uses 'agentic-testari' to generate test cases dynamically based on the application codebase. This should include both positive and negative tests.
   - **AI-Powered Test Execution**: Integrate 'agentic-testari' to execute these test cases autonomously, leveraging its AI to optimize the execution order and identify critical paths first.
   - **Result Analysis & Reporting**: Develop a module that analyzes the results of the tests, highlighting failures and suggesting improvements. This should also include generating comprehensive reports that can be easily shared.

5. **User Interface**: Create a simple yet effective command-line interface (CLI) for interacting with AutoTestSuite. This should allow users to input parameters for test scenarios, start tests, and view results directly from the CLI.

6. **Testing & Validation**: Rigorously test AutoTestSuite to ensure it functions as intended. Validate its effectiveness by comparing the outcomes against manual testing methods.

7. **Documentation & Deployment**: Write clear documentation detailing how to set up and use AutoTestSuite. Prepare it for deployment, ensuring it can be easily integrated into existing development environments.

By following these steps, you'll create a powerful, AI-driven tool that enhances the efficiency and effectiveness of QA testing processes.