testcato 1.2.5

A package for categorizing test results.

testcato

A Python package for automatically categorizing pytest test results (passed, failed, skipped) and enabling AI-assisted debugging of test failures.

Install

pip install testcato

Project Structure

• testcato/ - main package directory
  • categorizer.py - core logic for categorizing test results
  • llm_provider.py - module to add support for different large language models (LLMs)
• tests/ - unit tests for the package
• setup.py - package setup configuration
• requirements.txt - dependencies
• LICENSE - license file

Features

• Automatically categorize pytest test results into structured categories like passed, failed, and skipped.
• Generate detailed JSONL reports with tracebacks for failed tests, timestamped for easy reference.
• AI-assisted debugging support for failed tests using integrated GPT models.
• Easily add support for other LLM providers via a modular llm_provider system.
• Runs seamlessly with the --testcato pytest option to enable enhanced test reporting.
• Configuration via an automatically created testcato_config.yaml file for AI agent setup.

Usage with pytest

Run pytest with the --testcato flag to enable the collection and categorization of test results.

pytest --testcato

This will create a testcato_result folder in your working directory containing JSONL files with detailed failure information.

Configuration

The testcato_config.yaml file will be generated automatically when you first import or install the package, if it doesn't already exist. Configure your AI agents here, for example:

default: gpt

gpt:
  type: openai
  model: gpt-4
  api_key: YOUR_OPENAI_API_KEY
  api_url: https://api.openai.com/v1/chat/completions

Avoid committing API keys to version control; use environment variables or secret managers instead.

Adding Support for New LLM Providers

• To add support for other large language models, implement the provider interface in llm_provider.py, register your provider, and update the configuration.
• If the package uses a registry or factory pattern for LLM providers, add your new provider to the registry so it can be selected dynamically based on configuration or parameters.

Example provider skeleton:

class MyNewLLMProvider:
    def __init__(self, api_key):
        self.api_key = api_key

    def send_prompt(self, prompt):
        # Implement API call here
        response = ...  # Call API with prompt and api_key
        return response.get("text", "")

Testing

Unit tests are located in the tests/ directory. Install development dependencies and run tests using pytest:

pip install -r requirements.txt
pytest

Troubleshooting

If testcato_config.yaml is missing or malformed, AI debugging features will be disabled. Ensure the config file exists and is properly formatted.

When running pytest, use the --testcato option to enable testcato features:

pytest --testcato