WhatIF Analyzer

A powerful Python package for analyzing functions by automatically generating and running "what-if" scenarios, with a focus on edge cases and robustness testing.

🌟 Features

• 🔍 Static Analysis: Automatically infers parameter types and generates edge cases
• 🎯 Edge Case Testing: Tests with None, empty values, extreme numbers, and more
• 🧪 Lightweight Fuzzing: Optional fuzzing for additional test cases
• 📊 Detailed Reports: Human-readable summaries of test results
• 🛠️ Multiple Interfaces: Use as a decorator, CLI tool, or Python API
• 📝 Comprehensive Documentation: Clear examples and usage instructions

📋 Requirements

• Python 3.8 or higher
• Dependencies:
  • typing-extensions >= 4.0.0
  • inspect2 >= 0.1.0
  • rich >= 10.0.0
  • click >= 8.0.0
  • pytest >= 7.0.0 (for development)

🚀 Installation

From PyPI

pip install whatif-analyzer

From Source

git clone https://github.com/Arjunmehta312/what-if-python-package.git
cd what-if-python-package
pip install -e .

Development Installation

pip install -e .[dev]

📖 Quick Start

Using the Decorator

from whatif_analyzer import analyze

@analyze
def divide(a: float, b: float) -> float:
    return a / b

# The function will be automatically analyzed when called
result = divide(10, 2)

Using the CLI

# Analyze a Python file
whatif analyze path/to/your/file.py

# Analyze a specific function
whatif analyze path/to/your/file.py:function_name

Using the API

from whatif_analyzer import WhatIfAnalyzer

def my_function(x: int, y: str) -> bool:
    return len(y) > x

analyzer = WhatIfAnalyzer()
report = analyzer.analyze_function(my_function)
print(report)

📊 Example Report

from whatif_analyzer import analyze

@analyze
def process_data(data: list, threshold: int) -> float:
    if not data:
        return 0.0
    return sum(x for x in data if x > threshold) / len(data)

# The analysis will run automatically
result = process_data([1, 2, 3, 4, 5], 2)

Sample report output:

WhatIF Analysis Report
=====================

Function: process_data
Parameters:
  - data: list
  - threshold: int

Edge Cases Tested:
✓ Empty list
✓ None values
✓ Negative threshold
✓ Large threshold
✓ Mixed data types

Exceptions Found:
- TypeError: When data contains non-numeric values
- ZeroDivisionError: When data is empty and threshold is 0

Recommendations:
1. Add type checking for list elements
2. Handle empty list case explicitly
3. Consider adding input validation for threshold

🔧 Advanced Usage

Custom Edge Cases

from whatif_analyzer import WhatIfAnalyzer, EdgeCase

analyzer = WhatIfAnalyzer()

# Define custom edge cases
custom_cases = [
    EdgeCase("special_value", lambda x: x == 42),
    EdgeCase("negative_list", lambda x: all(i < 0 for i in x))
]

# Analyze with custom cases
report = analyzer.analyze_function(
    my_function,
    edge_cases=custom_cases
)

Configuration Options

from whatif_analyzer import WhatIfAnalyzer

analyzer = WhatIfAnalyzer(
    enable_fuzzing=True,
    max_fuzz_cases=100,
    timeout_seconds=5,
    verbose=True
)

🧪 Testing

Run the test suite:

pytest

Run with coverage:

pytest --cov=whatif_analyzer

📚 Documentation

For detailed documentation, visit our documentation site.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request