Source Code Energy Optimizer

Developer Names: Sevhena Walker, Mya Hussain, Ayushi Amin, Tanveer Brar, Nivetha Kuruparan Supervisor: Dr. David Istvan

Date of project start: September 16th 2024

Project Overview: The goal of this project is to develop tools to improve the energy efficiency of engineered software through refactoring without altering the intent of the source code.

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Key Features

1. Refactoring Library

   • Provides automated refactoring tools aimed at optimising code for energy efficiency while preserving its functional behaviour.
   • Analyses code to identify energy-intensive patterns and recommends or applies energy-saving transformations.
   • Ensures refactored code remains maintainable and efficient across different platforms.

2. Python-Specific Refactoring Optimization

   • Tailors energy-efficient refactoring strategies based on the specific characteristics of Python.
   • Provides guidelines and transformations to minimise energy consumption while maintaining code compatibility.
   • Adapts to the unique performance and energy model of Python.

3. Reinforcement Learning for Refactoring Preferences

   • Utilises reinforcement learning to adapt refactoring strategies based on past performance data.
   • Continuously improves the refactoring process by learning which transformations lead to the greatest energy savings.
   • Continuously improves the refactoring process by learning which transformations lead to most technically sustainable (readable) code.

4. DevOps GitHub Integration

   • Integrates with GitHub to automatically trigger energy-efficient refactoring as part of the CI/CD pipeline.
   • Provides version control, ensuring that refactoring changes can be tracked, tested, and validated before deployment.
   • Implements an automated feedback loop that records energy consumption data and feeds it back into the library's reinforcement learning model.
   • Automates testing of source code within the DevOps workflow to ensure that behaviour is maintained.

Nice-to-Have Features:

1. Library Plugin

   • Offers a plugin extension for popular IDEs and platforms, allowing developers to easily incorporate the refactoring library into their existing workflows.
   • Provides real-time suggestions and refactoring options within the development environment, enhancing usability and accessibility.
   • Synchronizes plugin with website allowing developers to view measurements taken in a visual manner (i.e. graphs, tables).

2. Human-in-the-Loop Reinforcement Learning

   • Involves human feedback in the reinforcement learning process to guide the system's refactoring decisions based on developer expertise and preferences.
   • Balances automated refactoring with human oversight to ensure that complex refactoring decisions align with the project's goals and constraints.

The folders and files for this project are as follows:

docs - Documentation for the project

refs - Reference material used for the project, including papers

src - Source code

test - Test cases

etc.