lupin-sw-ut-report 1.0.4.dev12

Tool used to generate a test reports.

lupin_sw_ut_report

This project converts test files (in .txt and .xml formats) into Markdown reports. The goal is to facilitate the documentation of software test results by providing readable Markdown files that can be used for comprehensive reporting.

Features

• TXT and XML File Conversion: Converts test files into structured Markdown files for better readability.
• Support for Given-When-Then Formats: Parses and converts test files defined using the Given, When, Then format.
• Combined Report Generation: Creates a single Markdown file summarizing all tests found in the specified folder.
• Command-Line Interface (CLI) with Typer: A CLI tool for easy execution of conversions.

Setting Up a Python Virtual Environment (Recommended for Development)

A Python virtual environment is an isolated workspace that allows you to install dependencies for a project without affecting your global Python installation or other projects. This is especially useful for development, as it helps avoid dependency conflicts and keeps your system clean.

Note: This setup is recommended for developers who want to contribute to or test the project locally. End users installing via pip install lupin-sw-ut-report do not need to follow these steps.

Why Use a Virtual Environment?

• Keeps project dependencies isolated from other Python projects and your system Python.
• Prevents version conflicts between packages.
• Makes it easy to manage and reproduce development environments.

Step-by-Step Setup (Windows)

1. Create a virtual environment in the project root:

   python -m venv .lupin_sw_ut_report
   

2. Activate the virtual environment:

   .\.lupin_sw_ut_report\Scripts\activate
   

   You should see the environment name (e.g., (.lupin_sw_ut_report)) appear in your terminal prompt.

3. Install the project in editable mode:

   pip install -e .
   

   This installs the project in "editable mode," meaning any changes you make to the source code will immediately affect your environment without needing to reinstall. This is ideal for development and testing.

4. Deactivate the virtual environment when done:

   deactivate
   

   This returns your terminal to the global Python environment.

Tip: The .lupin_sw_ut_report folder should be added to your .gitignore file to avoid committing it to version control.

Installation

Run pip install lupin-sw-ut-report

Usage

This project provides a command-line interface to generate reports from a folder containing test files (.txt and .xml).

To run the script, use the following command:

sw-ut-report --input-folder <path/to/your/input-folder>

Environment Variables

This project supports several environment variables for configuration, particularly for Jama integration and sandbox environments.

Jama Configuration Variables

These variables are required for Jama integration features:

# Required Jama connection settings
export JAMA_URL="your-jama-instance"
export JAMA_CLIENT_ID="your-client-id"
export JAMA_CLIENT_PASSWORD="your-client-password"
export JAMA_DEFAULT_PROJECT_ID="8"

Jama Test Set Configuration

Configure the test set container for unit test organization:

# Optional: Custom test set ID (defaults to "SmlPrep-SET-359")
export JAMA_TEST_SET_ID="YourCustom-SET-123"

Jama UT Test Case Configuration

Configure the test case ID used for finding the UT test group:

# Optional: Custom UT test case ID (defaults to "SmlPrep-UT-1")
export JAMA_UT_TEST_CASE_ID="YourCustom-UT-123"

Sandbox Search and Replace

For sandbox environments or when working with different Jama instances, you can configure automatic search and replace rules for cover IDs:

# Optional: Search and replace rules for cover IDs
export JAMA_SANDBOX_SEARCH_AND_REPLACE="sourceString1,replacement1;sourceString2,replacement2"

Search and Replace Format

The JAMA_SANDBOX_SEARCH_AND_REPLACE variable uses the following format:

• Multiple rules are separated by semicolons (;)
• Each rule has a source string and replacement string separated by comma (,)
• Whitespace around rules is automatically handled

Examples

Single rule:

export JAMA_SANDBOX_SEARCH_AND_REPLACE="SmlPrep,MyProject"

Multiple rules:

export JAMA_SANDBOX_SEARCH_AND_REPLACE="SmlPrep,MyProject;SET-359,SET-123;SUBSR,REQ"

With spaces (automatically handled):

export JAMA_SANDBOX_SEARCH_AND_REPLACE=" SmlPrep , MyProject ; SET-359 , SET-123 "

Transformation Examples

Input cover IDs:

• SmlPrep-SUBSR-123
• SmlPrep-SWID-456
• SmlPrep-SET-359

With JAMA_SANDBOX_SEARCH_AND_REPLACE="SmlPrep,MyProject;SET-359,SET-123":

Output cover IDs:

• MyProject-SUBSR-123
• MyProject-SWID-456
• MyProject-SET-123

Use Cases

• Sandbox Testing: Transform production IDs to sandbox equivalents
• Multi-Environment Support: Use different Jama instances with different naming conventions
• Migration: Transform old naming patterns to new ones
• Testing: Create test-specific transformations without modifying source files

Error Handling

• Invalid rule formats are logged and skipped
• Empty search or replacement strings are ignored
• Graceful handling of malformed environment variables
• Safe handling of None/empty inputs

Manual Publishing to PyPI

Note: For a fully automated deployment process, see the next section on using the provided PowerShell script.

To publish this package to PyPI, follow these manual steps:

1. Update the Version

You must update the version number in both of these files:

• src/sw_ut_report/__init__.py (e.g., __version__ = "0.1.0")
• pyproject.toml (e.g., version = "0.1.0")

Make sure the version numbers match in both files. This is required for a successful and consistent release.

2. Build the Package

Install the build tool if you haven't already:

pip install build

Run the following command from the root of the project:

python -m build --no-isolation

This will generate distribution files in the dist/ directory.

3. Prepare for Upload: PyPI Token and .pypirc

• Create an API token on your PyPI account.
• Create a .pypirc file in the root of your repository (but do not commit it to git!).
• The .pypirc file is already listed in .gitignore by default, but always double-check before committing.

Example .pypirc file:

[distutils]
index-servers =
    pypi

[pypi]
username = __token__  # Do not change this value; it must remain exactly as shown
password = <your-pypi-api-token-here>  # Provide your token without any quotes or extra characters

Replace <your-pypi-api-token-here> with your actual PyPI API token.

• Do not add any quotation marks (" or ') or extra characters around the token.
• The line username = __token__ must remain exactly as written.

Important:

• Never share your PyPI token.
• Never commit .pypirc to version control, even if it is already in .gitignore.

4. Upload to PyPI

Install Twine if you haven't already:

pip install twine

Upload your package using Twine and your .pypirc configuration:

twine upload --config-file ./.pypirc dist/*

If successful, your package will be published to PyPI.

Security Reminder

• Keep your PyPI API token secret.
• Do not share your .pypirc file or its contents.
• Always verify you are uploading the correct version and files.

Automated Publishing with PowerShell

You can automate the version update, build, and upload process using the provided PowerShell script:

Prerequisites

• Windows with PowerShell 7 or later
• Python installed and available in your PATH
• .pypirc file present in the project root (see above for details)

Usage

From the project root, run:

pwsh ./publish-to-pypi.ps1 -Version "0.1.2"

Replace 0.1.2 with your desired version number.

What the Script Does

• Checks for the presence of .pypirc and stops if missing
• Installs build and twine if not already installed
• Updates the version in both src/sw_ut_report/__init__.py and pyproject.toml
• Cleans the dist/ directory
• Builds the package
• Uploads the package to PyPI using your .pypirc configuration
• Stops and reports at the first error

This script streamlines the release process and helps ensure consistency between your code and published package.