README.md

Below is a markdown template for a README file tailored to your project, which involves a Python script leveraging the Vectr API to find test cases and create corresponding Jira tickets. The README is structured to provide clear instructions, project details, and setup guidance for users or contributors. I've included placeholders (e.g., [Your Project Name], [Your Jira Instance URL]) where you can fill in specific details about your project. The content is designed to be concise yet comprehensive, following best practices for README files and incorporating relevant context from the provided search results where applicable.

# [Your Project Name]

A Python script that uses the Vectr API to identify test cases and automatically create corresponding Jira tickets for test case management and tracking.

## Table of Contents
- [Project Overview](#project-overview)
- [Features](#features)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Configuration](#configuration)
- [Usage](#usage)
- [Script Details](#script-details)
- [Contributing](#contributing)
- [Reporting Issues](#reporting-issues)
- [License](#license)

## Project Overview
This project automates the process of finding test cases via the Vectr API and creating Jira tickets to track them. It is designed for QA teams or developers who need to streamline test case management within Jira, ensuring that test cases are systematically documented and actionable.

The script:
- Queries the Vectr API to retrieve test case data.
- Formats the test case data into a Jira-compatible payload.
- Creates Jira tickets using the Jira REST API with customizable fields (e.g., summary, description, issue type).

## Features
- **Vectr API Integration**: Retrieves test case details from Vectr's API.
- **Jira Ticket Creation**: Automatically creates Jira tickets with test case information.
- **Configurable**: Supports custom Jira fields and Vectr query parameters via a configuration file.
- **Error Handling**: Includes robust error handling for API failures and authentication issues.
- **Logging**: Logs actions and errors for debugging and auditing.

## Prerequisites
- Python 3.8 or higher
- A Vectr account with API access (API token required)
- A Jira account with API access (API token or username/password for authentication)
- [Optional] Virtual environment (recommended for dependency management)

## Installation
1. **Clone the Repository**:
   ```bash
   git clone https://github.com/[Your Username]/[Your Repository].git
   cd [Your Repository]

Set Up a Virtual Environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install Dependencies:
```
pip install -r requirements.txt
```
The requirements.txt file includes:
- requests (for API calls)
- python-dotenv (for environment variable management)
- [Add other dependencies as needed]

Configuration

Create a .env File: In the project root, create a .env file with the following variables:
```
VECTR_API_TOKEN=your_vectr_api_token
VECTR_API_URL=https://api.vectr.io/v1
JIRA_API_TOKEN=your_jira_api_token
JIRA_USERNAME=your_jira_username
JIRA_URL=https://[Your Jira Instance URL]/rest/api/2
JIRA_PROJECT_KEY=your_project_key
```
- Replace placeholders with your actual credentials and URLs.
- For Jira Cloud, use an API token (see Atlassian documentation).
- For Jira Server, you may use username and password instead of a token.
Update config.yaml (Optional): If you need custom Jira fields or Vectr query parameters, modify the config.yaml file:
```
jira:
  issue_type: Task
  custom_fields:
    customfield_12345: "Test Case"
vectr:
  query: "status:active"
  fields:
    - name
    - description
    - id
```
- Adjust fields based on your Jira project settings and Vectr API response structure.

Usage

Run the Script:
```
python main.py
```
The script will:
- Authenticate with the Vectr API and query test cases.
- Authenticate with the Jira API.
- Create a Jira ticket for each test case with formatted details.
Example Command with Options:
```
python main.py --config config.yaml --dry-run
```
- --config: Specify a custom configuration file.
- --dry-run: Simulate the process without creating tickets (logs output instead).

Sample Output:

[INFO] Retrieved 5 test cases from Vectr API.
[INFO] Creating Jira ticket for test case 'TC-001: Login Flow'...
[SUCCESS] Ticket PRJ-123 created.
[ERROR] Failed to create ticket for 'TC-002': Invalid custom field.

Script Details

File Structure:

├── main.py              # Main script for API calls and ticket creation
├── config.yaml          # Configuration file for Jira and Vectr settings
├── requirements.txt     # Python dependencies
├── .env                # Environment variables (not tracked in git)
├── README.md           # This file
└── logs/               # Directory for log files

Main Components:
- VectrClient: Handles authentication and queries to the Vectr API.
- JiraClient: Manages Jira API authentication and ticket creation.
- TestCaseProcessor: Maps Vectr test case data to Jira ticket fields.
Error Handling:
- Retries failed API requests up to 3 times.
- Logs detailed error messages to logs/script.log.
Extensibility:
- Add custom mappings in config.yaml for additional Jira fields.
- Modify main.py to support other Vectr API endpoints.

Contributing

Contributions are welcome! To contribute:

Fork the repository.
Create a feature branch (git checkout -b feature/your-feature).
Commit your changes (git commit -m "Add your feature").
Push to the branch (git push origin feature/your-feature).
Open a pull request.

Please include tests and update documentation as needed.

Reporting Issues

Report bugs or suggest features by creating a Jira ticket in our project:

[Create a Jira Ticket](https://[Your Jira Instance URL]/secure/CreateIssue!default.jspa?project=[Your Project Key])
Alternatively, open a GitHub issue at [Your Repository]/issues.

License

This project is licensed under the MIT License.

Generated based on best practices for Python projects and Jira/Vectr API integration.


### Notes on the README
- **Structure**: The README follows a standard format with sections for overview, setup, usage, and contribution, making it easy for users to navigate.
- **Placeholders**: Replace `[Your Project Name]`, `[Your Username]`, `[Your Repository]`, `[Your Jira Instance URL]`, and `[Your Project Key]` with your actual project details.
- **Assumptions**:
  - The Vectr API is accessible via `https://api.vectr.io/v1` (update if different).
  - Jira tickets are created using the `/rest/api/2/issue` endpoint, as per standard Jira REST API practices.[](https://developer.atlassian.com/server/jira/platform/jira-rest-api-examples/)
  - The script uses the `requests` library for API calls, common in Python scripts for Jira integration.[](https://www.merge.dev/blog/how-to-get-all-issues-with-the-jira-api-in-python)
- **Customization**:
  - The `config.yaml` file allows flexibility for custom Jira fields, inspired by tools like `jira-template`.[](https://github.com/mbrt/jira-template/blob/master/README.md)
  - The `.env` file ensures secure handling of API tokens, following security best practices.[](https://www.merge.dev/blog/how-to-get-all-issues-with-the-jira-api-in-python)
- **Error Handling and Logging**: Included to make the script robust, as recommended in Jira API scripts.[](https://stackoverflow.com/questions/77045427/using-python-to-automate-creation-of-jira-tickets)
- **Contributing and Issues**: Links to Jira for issue tracking align with your project's use of Jira for ticket management.

### Next Steps
- **Fill in Placeholders**: Update the README with your project-specific details.
- **Test the Script**: Ensure the script (`main.py`) aligns with the README's described functionality. If you need help generating the script itself, let me know!
- **Validate API Details**: Confirm the Vectr API URL and available endpoints, as the README assumes a generic structure.
- **Add License File**: Create a `LICENSE` file in your repository if using the MIT License or another license.
- **Version Control**: Ensure `.env` is added to `.gitignore` to prevent exposing sensitive tokens.

If you need the actual Python script (`main.py`) or additional details (e.g., specific Vectr API endpoints, custom Jira fields, or test case formats), please provide more context, and I can generate or refine those components. Let me know if you'd like to adjust the README further or include additional sections!