Introduction
In this tutorial, you'll learn how to use AI coding agents to modernize legacy research software. Based on recent findings from OpenAI and academic partners, these tools can dramatically speed up code modernization, but they require careful human oversight to ensure scientific accuracy. This tutorial will guide you through setting up an AI coding agent environment and demonstrate how to use it for code modernization while emphasizing the critical need for human verification.
Prerequisites
Before starting this tutorial, you should have:
- A basic understanding of Python programming
- Python 3.8 or higher installed on your computer
- Access to an AI coding agent platform (we'll use OpenAI's API as an example)
- A text editor or IDE (like VS Code or PyCharm)
- Basic knowledge of version control (Git)
Step-by-Step Instructions
1. Setting Up Your Environment
1.1 Install Required Python Packages
First, we need to set up our Python environment with the necessary packages. Open your terminal or command prompt and run:
pip install openai python-dotenvWhy we do this: The OpenAI API client library allows us to interact with AI coding agents, while python-dotenv helps manage API keys securely.
1.2 Create Your API Key
Sign up for an OpenAI account at platform.openai.com and generate an API key. Store this key in a secure location, as we'll need it to access the AI agent.
1.3 Create Environment Configuration
Create a file named .env in your project directory and add your API key:
OPENAI_API_KEY=your_api_key_hereWhy we do this: Storing API keys in environment variables keeps them secure and prevents accidental exposure in version control systems.
2. Creating a Sample Legacy Code
2.1 Write a Simple Legacy Python Script
Let's create a simple Python script that demonstrates old-style code. Create a file named legacy_script.py:
import math
def calculate_area(radius):
# Old-style calculation
area = 3.14159 * radius * radius
return area
def calculate_volume(radius, height):
# Old-style volume calculation
volume = 3.14159 * radius * radius * height
return volume
if __name__ == "__main__":
r = 5
h = 10
area = calculate_area(r)
volume = calculate_volume(r, h)
print("Area:", area)
print("Volume:", volume)Why we do this: This simple script represents typical legacy code that AI agents can modernize, such as using more precise math constants and better function structure.
3. Using AI Coding Agents for Modernization
3.1 Create a Modernization Script
Create a file named modernize_code.py that will use the AI agent to improve our legacy code:
import openai
import os
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
# Initialize OpenAI client
client = openai.OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
# Define the prompt for code modernization
prompt = '''
You are a code modernization expert. Please update the following legacy Python code to use modern Python practices:
1. Use math.pi instead of 3.14159
2. Add proper type hints
3. Use f-strings for output formatting
4. Improve function documentation
5. Add error handling
Here is the code to modernize:
```python
import math
def calculate_area(radius):
# Old-style calculation
area = 3.14159 * radius * radius
return area
def calculate_volume(radius, height):
# Old-style volume calculation
volume = 3.14159 * radius * radius * height
return volume
if __name__ == "__main__":
r = 5
h = 10
area = calculate_area(r)
volume = calculate_volume(r, h)
print("Area:", area)
print("Volume:", volume)
```
Please return only the modernized code without any explanations.
'''
# Call the AI agent
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a helpful assistant that modernizes code."},
{"role": "user", "content": prompt}
],
max_tokens=1000,
temperature=0.2
)
# Extract and display the modernized code
modernized_code = response.choices[0].message.content
print("Modernized Code:")
print(modernized_code)Why we do this: This script demonstrates how to interact with an AI coding agent to modernize code. The prompt specifically asks for improvements like using proper constants, type hints, and better formatting.
3.2 Run the Modernization Script
Execute your script:
python modernize_code.pyWhy we do this: Running the script will show you how AI agents can automatically suggest code improvements based on your prompts.
4. Reviewing and Verifying the AI Output
4.1 Understanding the Output
After running the script, you'll see output like:
import math
def calculate_area(radius: float) -> float:
"""Calculate the area of a circle given its radius."""
area = math.pi * radius * radius
return area
def calculate_volume(radius: float, height: float) -> float:
"""Calculate the volume of a cylinder given its radius and height."""
volume = math.pi * radius * radius * height
return volume
if __name__ == "__main__":
r = 5
h = 10
area = calculate_area(r)
volume = calculate_volume(r, h)
print(f"Area: {area}")
print(f"Volume: {volume}")Why we do this: Notice how the AI has improved the code by using math.pi, adding type hints, and implementing f-strings. However, we must verify that the scientific accuracy is still correct.
4.2 Manual Verification Process
Even though AI agents can modernize code quickly, it's crucial to verify the scientific correctness:
- Check that the mathematical formulas are correct
- Verify that the constants used are accurate
- Ensure that the logic flow hasn't been altered
- Confirm that the function behavior matches expected scientific results
Why we do this: As mentioned in the article, AI agents can be 'eloquent, convincing, and confidently wrong.' Manual verification is essential to ensure scientific accuracy.
5. Testing the Modernized Code
5.1 Create a Test Script
Create a file named test_modernized_code.py:
import math
def calculate_area(radius: float) -> float:
"""Calculate the area of a circle given its radius."""
area = math.pi * radius * radius
return area
def calculate_volume(radius: float, height: float) -> float:
"""Calculate the volume of a cylinder given its radius and height."""
volume = math.pi * radius * radius * height
return volume
# Test cases
assert calculate_area(5) == math.pi * 25
assert calculate_volume(5, 10) == math.pi * 25 * 10
print("All tests passed!")Why we do this: Testing ensures that the modernized code produces the correct scientific results, which is crucial when working with research software.
5.2 Run the Tests
Run your test script to verify the correctness:
python test_modernized_code.pyWhy we do this: This step confirms that our AI-modernized code still produces scientifically accurate results.
Summary
This tutorial demonstrated how to use AI coding agents for modernizing research software. We set up a Python environment, created a legacy code example, used an AI agent to modernize it, and then verified the results. While AI agents can dramatically speed up code modernization (with potential 60x speedups), they require careful human oversight to ensure scientific accuracy. The key takeaway is that AI tools are powerful assistants, but they cannot replace human judgment in scientific contexts. Always verify the scientific correctness of AI-generated code before using it in research applications.
