Solidity-Gas-Optimizoor

An high performance automated tool that optimizes gas usage in Solidity smart contracts, focusing on storage and function call efficiency.

For more information on architecture and implementation, see the docs

Warning

This code is a work in progress and can contain bugs. Use it at your own risk. Feature request and bug reports are welcome.

Dev Writeup for design, architecture and process can be found here

Requirements

• >= go1.22.2 link
• >= node 18 link
• foundryup link
• make
• tmux (optional)

Installation

1. Clone the Repository

git clone --recurse-submodules https://github.com/yongkangc/solidity-optimiser-app.git

2. Build the Go Project by running make build

Note

Check out the installation guide for detailed instructions on setting up the project.

Usage

• CLI: run ./build/optimizer --help in project root to get started with the CLI tool.
• Web App: run make start to start the backend and frontend with tmux.
  • View the frontend at http://localhost:3000

Note

More information at usage guide.

Running Tests

make optimizer-test

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Project Information

Problem Statement:

Solidity developers need tools to help them write gas-efficient code to minimize the execution cost of smart contracts on the blockchain. While there are some linters and optimizers available, there's a lack of tools specifically designed to analyze and suggest optimizations at both the source code and intermediate representation levels.

Project Objective:

The goal of this project is to design and implement a CLI and Web app that analyzes Solidity smart contracts, identifies patterns that lead to high gas usage, and suggests or automatically applies optimizations to improve gas efficiency.

How does our tool optimize the gas of your smart contracts?

Tip

See some examples here.

Struct Data Packing

• Concept: Aligning struct members under 32 bytes together optimizes storage usage on the EVM.
• Advantages: This technique minimizes the number of SLOAD or SSTORE operations, slashing storage interaction costs by 50% or more when dealing with multiple struct values within a single slot.
• Documentation: Structured Data Packing Guidance

Demo

Caching Storage Variables

• Approach: Utilize local variables to cache frequently accessed storage variables, reducing the number of expensive storage reads and writes.
• Details: Create a temporary local variable to store the value of a storage variable if it's accessed multiple times.
• Source: Caching Storage Variables

Calldata Efficiency

• Gas Savings: Leveraging calldata for unaltered external function inputs is more cost-effective than utilizing memory.
• Implementation: Analyze functions to ensure that inputs declared as memory are not modified. If unmodified, convert to calldata.
• Reference: Calldata Efficiency Tips