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https://github.com/heaversm/refactor-codebert


https://github.com/heaversm/refactor-codebert

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README 

        
# RefactorEarth



## Executive Summary



RefactorEarth is a project aimed at analyzing and optimizing Python code to improve its efficiency and sustainability. By leveraging machine learning models and sustainability tracking tools such as CodeCarbon, RefactorEarth identifies memory-intensive operations, redundant computations, and suboptimal data structures, and suggests improvements. The project's key achievements include significant reductions in energy consumption and carbon footprint. RefactorEarth is currently in the beta testing phase (version 0.9), actively seeking feedback from users.



## Table of Contents



1. [Executive Summary](#executive-summary)

2. [Introduction](#introduction)

3. [Objective](#objective)

4. [Methodology](#methodology)

    1. [Repository Analysis](#repository-analysis)

    2. [AI-Powered Refactoring](#ai-powered-refactoring)

5. [Key Performance Indicators (KPIs)](#key-performance-indicators-kpis)

6. [Sustainability Score Calculation](#sustainability-score-calculation)

7. [Technical Stack](#technical-stack)

8. [Installation and Setup](#installation-and-setup)

9. [Usage Examples](#usage-examples)

10. [Architecture Overview](#architecture-overview)

11. [Contribution Guidelines](#contribution-guidelines)

12. [API Documentation](#api-documentation)

13. [Frequently Asked Questions (FAQ)](#frequently-asked-questions-faq)

14. [Case Studies](#case-studies)

15. [Future Roadmap](#future-roadmap)

16. [Glossary](#glossary)

17. [Related Projects and Tools](#related-projects-and-tools)

18. [Security Considerations](#security-considerations)

19. [Known Limitations](#known-limitations)

20. [Getting Help](#getting-help)

21. [Best Practices](#best-practices)

22. [Version History](#version-history)

23. [References](#references)



## Introduction



RefactorEarth is designed to analyze and improve the efficiency of Python code by identifying memory-intensive operations, redundant computations, and suboptimal data structures. By leveraging advanced machine learning models and sustainability tracking tools like CodeCarbon, RefactorEarth aims to assist developers in writing more sustainable and performant code.



### Project Background



Modern software development, while enabling rapid innovation, often overlooks the sustainability aspect of coding practices. Inefficient code not only slows down performance but also leads to excessive energy consumption, contributing to a larger carbon footprint. This issue is particularly acute in large-scale applications and data centers, where the cumulative impact of inefficient code can result in significant energy wastage.



### Common Issues in Unsustainable Code



1. **Memory-Intensive Operations**: Large, unnecessary memory allocations that strain system resources.

2. **Redundant Computations**: Repeated calculations that could be optimized or cached.

3. **Suboptimal Data Structures**: Use of inefficient data structures that increase processing time and energy consumption.

4. **Inefficient Loops**: Poorly optimized loops that lead to higher CPU utilization and energy consumption.

5. **Inefficient I/O Operations**: Excessive or unoptimized input/output operations that degrade performance.



These inefficiencies not only degrade performance but also significantly increase the energy required to run software applications, making them less sustainable. RefactorEarth aims to address these issues by providing targeted optimizations that reduce the environmental impact of software development.



## Objective



The primary objective of RefactorEarth is to optimize Python code for reduced energy consumption and improved sustainability metrics. This is achieved by analyzing existing codebases and suggesting optimizations that enhance efficiency and reduce the digital carbon footprint.



## Methodology



### Repository Analysis



1. **Clone Target Repository**: Identify and clone the target repository containing Python files.

2. **Initial Metrics Calculation**: Measure initial energy consumption, carbon footprint, and sustainability score using the CodeCarbon tool.



### AI-Powered Refactoring



1. **Base Model**: Utilize Microsoft's CodeBERT as the foundational model.

2. **Fine-Tuning**: Fine-tune the model on a custom dataset of energy-efficient Python code.

3. **Optimization Focus**: Emphasize loop efficiency, memory usage, and I/O operations.

4. **Personalization**: Incorporate data from the repository owner's GitHub profile to tailor optimizations.

5. **Iterative Optimization**: Conduct multiple passes to refine and improve code efficiency continuously. The model identifies optimization opportunities by analyzing the code structure, usage patterns, and comparing them with best practices in energy-efficient coding.



## Key Performance Indicators (KPIs)



The following KPIs are used to measure the effectiveness of the optimizations:



- **Energy Consumption**: Measured in joules (J) using CodeCarbon.

- **Carbon Footprint**: Measured in grams of CO2 equivalent (gCO2) using CodeCarbon.

- **Sustainability Score**: Scored from 0 to 100.

- **CPU Time**: Measured in seconds.

- **Equivalent Smartphone Charge**: Measured in percentage (%).

- **LED Bulb Power Time**: Measured in seconds for a 10W bulb.



## Sustainability Score Calculation



The Sustainability Score is a composite metric calculated based on energy efficiency and carbon footprint, normalized against predefined maximum values.



Here is the Python code to calculate the sustainability score:



```python

def calculate_sustainability_score(energy_consumption, carbon_footprint, max_energy_consumption, max_carbon_footprint):

    normalized_energy = energy_consumption / max_energy_consumption

    normalized_carbon = carbon_footprint / max_carbon_footprint

    sustainability_score = (1 - normalized_energy) * 0.5 + (1 - normalized_carbon) * 0.5

    return sustainability_score * 100

```



### Using CodeCarbon to Measure Metrics



CodeCarbon is used to measure energy consumption and carbon footprint. Here is how you can use CodeCarbon to obtain these metrics:



```python

from codecarbon import EmissionsTracker



tracker = EmissionsTracker()

tracker.start()



# Place the code you want to measure here

# ...



tracker.stop()



emissions_data = tracker.final_emissions_data

energy_consumed = emissions_data.energy_consumed  # in kWh

carbon_emissions = emissions_data.emissions  # in kgCO2



# Convert energy to joules (1 kWh = 3.6e6 J)

energy_consumed_joules = energy_consumed * 3.6e6

```



## Technical Stack



- **AI Model**: Fine-tuned CodeBERT (Version: 2.0)

- **Language**: Python (Version: 3.8+)

- **Version Control**: Git

- **Repository Analysis**: GitHub API (Version: 1.59.0)

- **Sustainability Tracking**: CodeCarbon (Version: 1.0.0)

- **Deployment**: Docker containerization (Version: 20.10.7)



### Technology Choices



- **CodeBERT**: Chosen for its capability in understanding and generating code.

- **Python**: Widely used in data science and AI, making it suitable for this project.

- **GitHub API**: Essential for accessing and analyzing repositories.

- **CodeCarbon**: Provides accurate measurements of energy consumption and carbon footprint.

- **Docker**: Ensures consistent deployment across different environments.



## Installation and Setup



### Prerequisites



Ensure you have the following installed:



- Python 3.8+

- pip (Python package installer)



### Environment Variables



Create a `.env` file in the root directory and add your GitHub token:



```

GITHUB_TOKEN=your_github_token

```



Load the environment variables:



```sh

export $(cat .env | xargs)

```



### Dependencies



Install the required dependencies:



```sh

pip install -r requirements.txt

```



### `requirements.txt`



```

codecarbon

PyYAML

torch==2.0.0

transformers==4.30.0

datasets==2.3.0

PyGithub==1.59.0

python-dotenv==1.0.0

```



### Troubleshooting Tips



1. **Dependency Installation Failures**

   - Ensure you have the correct Python version.

   - Use a virtual environment to avoid conflicts with system packages.

   - Verify your internet connection for downloading packages.



2. **GitHub Token Not Found**

   - Ensure the `.env` file is correctly formatted.

   - Reload the environment variables using `export $(cat .env | xargs)`.



3. **CodeCarbon Not Tracking Emissions**

   - Ensure your system supports energy consumption tracking.

   - Check if CodeCarbon dependencies are properly installed.



### System Requirements



- **Operating Systems**: Windows, macOS, Linux

- **Minimum RAM**: 4 GB

- **Processor Speed**: 2 GHz or higher



### Verifying Successful Installation



1. **Run Tests**



```sh

pytest tests/

```



2. **Check Dependencies**



```sh

pip check

```



3. **Execute a Sample Optimization**



```sh

python refactor.py --repo_url  --github_token 

```



## Usage Examples



### Command-Line Usage



To analyze and optimize a repository, use the following command:



```sh

python



 refactor.py --repo_url  --github_token 

```



### API Usage



If applicable, RefactorEarth can be integrated into other tools via an API. Example:



```python

from refactor_earth import RefactorEarth



# Initialize the tool

refactor = RefactorEarth(github_token="your_github_token")



# Optimize a repository

results = refactor.optimize_repo("https://github.com/user/repo")



# Print the results

print(results)

```



### Example Scenarios



#### Web Application Optimization



**Before Optimization:**



```python

from flask import Flask



app = Flask(__name__)



@app.route('/')

def index():

    total = 0

    for i in range(10000):

        total += i

    return str(total)



if __name__ == '__main__':

    app.run()

```



**After Optimization:**



```python

from flask import Flask



app = Flask(__name__)



@app.route('/')

def index():

    total = sum(range(10000))

    return str(total)



if __name__ == '__main__':

    app.run()

```



#### Data Analysis Script



**Before Optimization:**



```python

import pandas as pd



def analyze_data(file_path):

    df = pd.read_csv(file_path)

    results = []

    for index, row in df.iterrows():

        result = row['A'] * row['B']

        results.append(result)

    return results



analyze_data('data.csv')

```



**After Optimization:**



```python

import pandas as pd



def analyze_data(file_path):

    df = pd.read_csv(file_path)

    results = df['A'] * df['B']

    return results.tolist()



analyze_data('data.csv')

```



### Interpreting Results



- **Energy Consumption**: Lower values indicate more efficient code.

- **Carbon Footprint**: Reduced carbon emissions reflect better sustainability.

- **Sustainability Score**: Higher scores signify improved overall sustainability.



## Architecture Overview



The architecture of RefactorEarth involves several components, including:



- **Data Ingestion**: Cloning repositories and extracting relevant Python files.

- **Initial Analysis**: Using CodeCarbon to measure initial metrics.

- **Model Training**: Fine-tuning CodeBERT on a dataset of energy-efficient code.

- **Optimization Engine**: Applying the model to suggest and implement code optimizations.

- **Metrics Evaluation**: Re-evaluating the code using CodeCarbon after optimization.



### Integration Points



- **GitHub API**: For fetching repository data.

- **CodeCarbon**: For tracking energy consumption and carbon footprint.



## Contribution Guidelines



### Code Style Guidelines



- Follow PEP 8 for Python code.

- Use meaningful variable names and comments.



### Testing Requirements



- Write unit tests for new features.

- Ensure all tests pass before submitting a pull request.



### Pull Request Process



1. Fork the repository.

2. Create a new branch:



```sh

git checkout -b feature-branch

```



3. Make your changes.

4. Commit your changes:



```sh

git commit -am 'Add new feature'

```



5. Push to the branch:



```sh

git push origin feature-branch

```



6. Create a new Pull Request.



### Code Review Criteria



- Code readability and maintainability.

- Adherence to coding standards.

- Comprehensive test coverage.



## API Documentation



If RefactorEarth exposes any APIs, include comprehensive API documentation with:



### Endpoint Descriptions



- `/optimize`: Optimizes a given repository.

- `/metrics`: Retrieves sustainability metrics.



### Request/Response Formats



```json

{

  "repo_url": "https://github.com/user/repo",

  "github_token": "your_github_token"

}

```



### Example Response



```json

{

  "energy_consumption": 20.00,

  "carbon_footprint": 0.003080,

  "sustainability_score": 80.0,

  "cpu_time": 0.00020,

  "smartphone_charge": 0.00044,

  "led_bulb_time": 0.00100

}

```



### Authentication Requirements



- GitHub token is required for accessing private repositories.



### Rate Limiting Information



- Limited to 100 requests per hour.



### Error Handling and Response Codes



- **200 OK**: Request succeeded.

- **400 Bad Request**: Invalid input parameters.

- **401 Unauthorized**: Invalid or missing GitHub token.

- **500 Internal Server Error**: Server encountered an error.



## Frequently Asked Questions (FAQ)



1. **What is RefactorEarth?**

   - RefactorEarth is a tool for analyzing and optimizing Python code to improve efficiency and sustainability.



2. **How does RefactorEarth measure energy consumption?**

   - It uses CodeCarbon to track energy consumption and carbon footprint.



3. **Do I need a GitHub token to use RefactorEarth?**

   - Yes, a GitHub token is required to access repositories.



4. **Can RefactorEarth optimize any Python code?**

   - RefactorEarth is designed to work with most Python codebases, but the effectiveness may vary based on the code's complexity.



5. **How is the Sustainability Score calculated?**

   - The Sustainability Score is a composite metric based on normalized energy consumption and carbon footprint.



6. **What Python versions are supported?**

   - RefactorEarth supports Python 3.8 and above.



7. **Can RefactorEarth be integrated with CI/CD pipelines?**

   - Yes, RefactorEarth can be integrated into CI/CD pipelines for continuous optimization.



## Case Studies



### Project A: Data Processing Pipeline



#### Background



An open-source data processing library was experiencing high energy consumption and suboptimal performance. The maintainers wanted to improve the library's efficiency and sustainability metrics.



#### Before Optimization



**Code Snippet:**



```python

import pandas as pd



def process_data(file_path):

    data = pd.read_csv(file_path)

    processed_data = []

    for index, row in data.iterrows():

        result = row['column1'] * row['column2']

        processed_data.append(result)

    return processed_data



file_path = 'data.csv'

result = process_data(file_path)

```



#### After Optimization



**Optimized Code:**



```python

import pandas as pd



def process_data(file_path):

    data = pd.read_csv(file_path)

    processed_data = data['column1'] * data['column2']

    return processed_data.tolist()



file_path = 'data.csv'

result = process_data(file_path)

```



### Project B: Machine Learning Model Training



#### Background



A machine learning project involved training models that were computationally intensive. The goal was to reduce the environmental impact of the training process.



#### Before Optimization



**Code Snippet:**



```python

import tensorflow as tf

import numpy as np



def train_model(data, labels):

    model = tf.keras.models.Sequential([

        tf.keras.layers.Dense(128, activation='relu'),

        tf.keras.layers.Dense(64, activation='relu'),

        tf.keras.layers.Dense(10, activation='softmax')

    ])

    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

    model.fit(data, labels, epochs=50)

    return model



data = np.random.rand(1000, 20)

labels = np.random.randint(10, size=1000)

model = train_model(data, labels)

```



#### After Optimization



**Optimized Code:**



```python

import tensorflow as tf

import numpy as np



def train_model(data, labels):

    model = tf.keras.models.Sequential([

        tf.keras.layers.Dense(64, activation='relu'),

        tf.keras.layers.Dense(32, activation='relu'),

        tf.keras.layers.Dense(10, activation='softmax')

    ])

    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

    model.fit(data, labels, epochs=30)

    return model



data = np.random.rand(1000, 20)

labels = np.random.randint(10, size=1000)

model = train_model(data, labels)

```



## Future Roadmap



### Upcoming Features



- Support for additional programming languages.

- Integration with more sustainability tracking tools.



### Long-Term Goals



- Establish a community-driven database of optimized code patterns.

- Develop educational resources on sustainable coding practices.



### Potential Challenges



- Ensuring compatibility with a wide range of Python codebases.

- Maintaining accuracy and reliability of sustainability metrics.



### Opportunities for Community Involvement



- Contribute code optimizations.

- Participate in beta testing of new features.

- Share success stories and case studies.



## Glossary



- **CodeCarbon**: A tool for measuring the carbon footprint of code execution.

- **CodeBERT**: A pre-trained model for programming language understanding and generation.

- **Sustainability Score**: A composite score from 0 to 100, reflecting the energy efficiency and environmental impact of code.



## Related Projects and Tools



- **Green AI**: Focuses on reducing the environmental impact of AI research.

- **CarbonTracker**: Tracks and reports the carbon emissions of machine learning models.

- **EcoCode**: Analyzes and improves the sustainability of software projects.



## Security Considerations



### Handling Sensitive Data



- RefactorEarth does not store sensitive data



.

- All data processing occurs locally on the user's machine.



### Security Best Practices for Users



- Regularly update dependencies to mitigate vulnerabilities.

- Use secure methods for storing and accessing GitHub tokens.



### Vulnerability Reporting Process



- Report security vulnerabilities via email to [email protected].



### Frequency of Security Audits



- Security audits are performed quarterly to ensure the system remains secure against new vulnerabilities.



## Known Limitations



- **Not Suitable for All Codebases**: RefactorEarth may not be effective for codebases that are already highly optimized or use non-standard coding practices.

- **Performance Overhead**: The optimization process may introduce some performance overhead, especially for very large projects.

- **Known Issues**: Current limitations include handling large monolithic codebases efficiently.



## Getting Help



- **Discord Channel**: Join the discussion on our Discord channel for community support and interaction.



## Best Practices



- **Integrate RefactorEarth into CI/CD Pipelines**: Automate the optimization process for continuous improvement.

- **Regularly Update Dependencies**: Keep your dependencies up to date to leverage the latest features and improvements.

- **Review Optimization Suggestions**: Manually review the suggestions to ensure they align with your project goals and coding standards.



## Version History



- **Version 0.9 (Beta)**: Initial release for beta testing, including core functionalities and initial set of optimization features.

- **Version 1.0**: Planned features include support for additional programming languages and enhanced optimization techniques based on community feedback.



## References



1. **CodeCarbon**: CodeCarbon is a tool that estimates the amount of carbon dioxide (CO2) produced by the electricity required to run computer programs. [CodeCarbon Documentation](https://codecarbon.io/documentation)

2. **Microsoft's CodeBERT**: CodeBERT is a pre-trained model for programming language understanding and generation, developed by Microsoft. [CodeBERT Research Paper](https://arxiv.org/abs/2002.08155)



## Repository Structure



Here is an overview of the important files and directories in the RefactorEarth repository:



- `refactor.py`: Main script for running the optimization process.

- `requirements.txt`: List of dependencies required to run the project.

- `README.md`: Project documentation.

- `config/`: Directory containing configuration files.

- `data/`: Directory for storing datasets and intermediate data.

- `models/`: Directory for storing pre-trained and fine-tuned models.

- `notebooks/`: Jupyter notebooks for exploratory data analysis and testing.

- `scripts/`: Additional scripts for various tasks (e.g., data preprocessing).

- `tests/`: Unit tests for the project.



### Key Files



1. **refactor.py**



   The main script to run the optimization process on a given repository.



   ```sh

   python refactor.py --repo_url  --github_token 

   ```



2. **requirements.txt**



   Contains all the dependencies needed for the project. Install these using:



   ```sh

   pip install -r requirements.txt

   ```



3. **config/config.yaml**



   Configuration file for setting various parameters for the optimization process.



4. **models/codebert_finetuned.pth**



   Fine-tuned CodeBERT model used for the optimization process.



5. **scripts/preprocess.py**



   Script for preprocessing data before feeding it into the model.



6. **tests/test_refactor.py**



   Unit tests to ensure the optimization process works correctly.



### Using the Repository



1. **Clone the Repository**



   ```sh

   git clone https://github.com/yourusername/refactor-earth.git

   cd refactor-earth

   ```



2. **Install Dependencies**



   ```sh

   pip install -r requirements.txt

   ```



3. **Set Up Environment Variables**



   Create a `.env` file and add your GitHub token:



   ```

   GITHUB_TOKEN=your_github_token

   ```



   Load the environment variables:



   ```sh

   export $(cat .env | xargs)

   ```



4. **Run the Optimization**



   ```sh

   python refactor.py --repo_url  --github_token 

   ```



5. **Run Tests**



   Ensure everything is working correctly by running the tests:



   ```sh

   pytest tests/

   ```



By following these instructions, users can effectively use RefactorEarth to analyze and optimize their Python code for better efficiency and sustainability.