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Add AI-Assisted Code Refactoring module with introductory notebooks and exercises
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‎04-refactoring/README.md

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# AI-Assisted Code Refactoring Module
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This directory contains notebooks for learning code refactoring using AI assistance tools like GitHub Copilot. The notebooks provide hands-on exercises to improve code quality through modern programming practices.
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## Notebooks
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- [`refactoring - easy.ipynb`](refactoring%20-%20easy.ipynb): An introductory notebook focusing on basic refactoring principles using a simple GradeCalculator class. Perfect for beginners learning about code improvement techniques.
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- [`refactoring - intermediate.ipynb`](refactoring%20-%20intermediate.ipynb): A more advanced notebook using a SoilDataProcessor class that demonstrates complex refactoring scenarios with real-world data processing examples.
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## Getting Started
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To begin the refactoring workshop:
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1. Ensure you have access to GitHub Copilot in your development environment
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2. Open the notebooks in order (easy → intermediate)
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3. Follow the exercises and prompts within each notebook
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4. Try the refactoring suggestions and experiment with your own improvements
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## Workshop Structure
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Each notebook follows a consistent structure:
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1. Initial code presentation
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2. Code analysis exercise
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3. Guided refactoring using Copilot
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4. Prompt crafting practice
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5. Discussion of improvements
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## Learning Objectives
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Through these notebooks, participants will learn:
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- How to identify code that needs improvement
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- Effective use of AI tools for code refactoring
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- Modern Python programming practices including:
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- Using pandas for data processing
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- Implementing proper error handling
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- Adding type hints and documentation
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- Using logging instead of print statements
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- Writing more maintainable code

‎04-refactoring/refactoring - easy.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# AI-Assisted Code Refactoring Workshop\n",
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"## Using GitHub Copilot for Code Improvement\n",
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"\n",
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"This notebook will guide you through practical exercises in code refactoring using AI assistance tools like GitHub Copilot."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Activity 1: Refactoring OOP-Based Python Code\n",
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"\n",
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"First, let's examine a sample `SoilDataProcessor` class that needs improvement. This class represents common patterns that could benefit from refactoring."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"class GradeCalculator:\n",
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" def __init__(self):\n",
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" self.grades = []\n",
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" \n",
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" def add_grade(self, grade):\n",
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" \"\"\"Add a grade\"\"\"\n",
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" self.grades.append(grade)\n",
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" \n",
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" def get_average(self):\n",
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" \"\"\"Get average grade\"\"\"\n",
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" if len(self.grades) == 0:\n",
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" return 0\n",
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" return sum(self.grades) / len(self.grades)\n",
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" \n",
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" def print_summary(self):\n",
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" \"\"\"Print grade summary\"\"\"\n",
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" print(f\"Total grades: {len(self.grades)}\")\n",
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" print(f\"Average: {self.get_average():.2f}\")\n",
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" print(f\"Highest: {max(self.grades)}\")\n",
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" print(f\"Lowest: {min(self.grades)}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Example usage:\n",
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"calculator = GradeCalculator()\n",
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"\n",
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"calculator.add_grade(85)\n",
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"calculator.add_grade(90)\n",
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"calculator.add_grade(78)\n",
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"\n",
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"calculator.print_summary()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 🔍 Exercise 1: Code Analysis\n",
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"Take a moment to review the code above and identify potential issues:\n",
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"1. What are the main inefficiencies?\n",
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"2. Where could error handling be improved?\n",
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"3. How might you improve the design?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Your analysis here\n",
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"\n",
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"\"\"\"\n",
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"\n",
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"\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 💡 Exercise 2: Refactoring with Copilot\n",
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"Now, let's use GitHub Copilot to help refactor this code. Here are some prompts to try:\n",
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"\n",
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"1. General improvement prompt:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'\\n\\nRefactor this GradeCalculator class to use pandas and follow better design principles\\n\\n'"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\"\"\"\n",
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"\n",
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"Refactor this GradeCalculator class to use pandas and follow better design principles\n",
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"\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"2. Specific enhancement prompt:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'\\n\\nRefactor this class to:\\n1. Use pandas for efficient data processing\\n2. Implement proper error handling with custom exceptions\\n3. Add type hints and documentation\\n4. Implement logging instead of print statements\\n\\n'"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\"\"\"\n",
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"\n",
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"Refactor this class to:\n",
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"1. Use pandas for efficient data processing\n",
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"2. Implement proper error handling with custom exceptions\n",
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"3. Add type hints and documentation\n",
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"4. Implement logging instead of print statements\n",
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"\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Activity 2: Crafting Effective Refactoring Prompts\n",
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"\n",
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"Now that you've seen some example prompts, let's practice creating our own. Consider what makes a prompt effective:\n",
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"- Specificity about desired improvements\n",
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"- Clear constraints and requirements\n",
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"- Examples of expected behavior"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Your prompts here:\n",
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"\"\"\"\n",
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"1.\n",
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"\n",
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"2.\n",
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"\n",
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"3.\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Conclusion\n",
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"Remember these key takeaways when working with AI coding assistants:\n",
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"1. Start with clear, specific prompts\n",
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"2. Review and understand the suggested changes\n",
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"3. Verify that the refactored code maintains the original functionality\n",
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"4. Test the refactored code thoroughly\n",
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"5. Use AI suggestions as a starting point and apply your own judgment\n",
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"\n",
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"_Note: This is a learning exercise. In real-world applications, additional considerations like testing, documentation, and specific requirements would need to be addressed._"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "githubcopilotworkshop2",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.14"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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