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Augment Swebench Agent

Welcome to the Augment Swebench Agent repository! This project is part of the Dimvy Clothing Brand's initiative to augment and automate workflows using advanced agent-based technologies.

🚀 Overview

The Augment Swebench Agent is designed to enhance software development productivity by integrating with Swebench and automating repetitive tasks, error detection, and code management. It aims to provide tools for:

• Automated error fixing and code linting
• Execution and testing of code changes
• Security and vulnerability detection
• Project and codebase management
• Duplicate code detection and resolution

🛠️ Features

• Error Detection & Fixing: Automatically finds and suggests fixes for common code errors.
• Execution & Testing: Runs and verifies code changes in a secure, sandboxed environment.
• Security Tools: Scans for vulnerabilities and hides sensitive code from exposure.
• Code Management: Supports configuration, project management, and automation tasks.
• Duplicate Code Finder: Detects and helps resolve duplicate code segments in the codebase.

📦 Installation

Clone this repository:

git clone https://github.com/Dimvy-Clothing-brand/augment-swebench-agent.git
cd augment-swebench-agent

Install dependencies (example shown for Python projects):

pip install -r requirements.txt

Main You can increase --num-examples and --num-candidate-solutions to run on more problems and generate more candidate solutions. But be aware that this will take longer and cost more money.

Command-line Options

• --num-examples: Number of examples to run on (default: None, which runs on all examples)
• --shard-ct: Number of shards to split the work into (default: 1)
• --shard-id: Shard ID to run (0-indexed, default: 0)
• --num-processes: Number of processes to use for each example (default: 8)
• --num-candidate-solutions: Number of candidate solutions to generate for each example (default: 8)

Running on more examples.

There are 500 examples total in SWE-bench Verified. Note that this can take awhile, so there are a few levels of parallelism this repository supports.

• Firstly, we suggest running 8 processes. This is the --num-processes flag. Beyond this, Docker hits issues.
• Secondly, we support a notion of breaking up the dataset into shards. This is the --shard-ct and --shard-id flags. This makes it relatively easy to split up the work across multiple machines, which circumnvents the issues with scaling Docker beyond 8 processes.

In our experiments, it took us a couple hours to run the full evaluation for 1 candidate solution per problem. This was with 10 shards split out across separate pods (managed by Kubernetes) and each pod had 8 processes.

Keep in mind that you may hit rate-limits from Anthropic running 80 agents in parallel like we did. We have very high rate-limits with Anthropic's API that you may not have. Given this, you may have to run with a smaller --shard-ct and/or --num-processes.

Suppose you want to run with 10 shards and 8 processes per shard, then that would mean you run the following command 10 times, varying the --shard-id flag from 0 to 9, on 10 different machines:

python run_agent_on_swebench_problem.py --shard-ct 10 --shard-id <worker_index> > logs.out 2> logs.err

Majority Vote Ensembler

The Majority Vote Ensembler is a tool that helps select the best solution from multiple candidates using an LLM. It works by presenting multiple candidate solutions to a problem to OpenAI's o1 model and asking it to analyze and select the most common solution.

How It Works

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Note: Make sure to check the specific dependencies and environment requirements in the requirements.txt or project documentationmain

⚡ Usage

You can run the agent using the following command (modify as per your main entry point):

python main.py

For advanced configuration, see the config/ directory or refer to the project documentation.

🔒 Security

• The agent includes security checks to prevent code injection and data leaks.
• Sensitive code and credentials should be stored in .env files or secured configuration.
• Always review and test automated fixes before deploying to production.

💬 Contributing

We welcome contributions! To contribute:

1. Fork the repository.
2. Create a new branch for your feature or fix.
3. Commit your changes and open a pull request.

Please refer to the CONTRIBUTING.md (or open an issue for guidelines if not present).

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

📧 Contact

For project inquiries or paid collaborations, please contact:
Email: contact@dimvyclothing.com
GitHub: Dimvy-Clothing-brand

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Empowering developers, automating excellence. =======

Augment SWEBench Agent

The #1 open-source SWE-bench Verified implementation.

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Table of Contents

• About
• Features
• Installation
• Usage
• Contributing
• License
• Acknowledgments

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About

The Augment SWEBench Agent is an open-source project aimed at providing a verified implementation of SWE-Bench, a tool designed to enhance software engineering workflows. This forked version inherits and builds upon the original repository, offering additional customizations and improvements.

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Features

• Fully compliant with SWE-Bench standards.
• Written in Python for ease of extensibility.
• Open-source under the MIT license.
• Seamlessly integrates with various CI/CD pipelines.

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Installation

1. Clone the repository:

   git clone https://github.com/nodoubtz/augment-swebench-agent.git
   cd augment-swebench-agent

2. Install dependencies:

   pip install -r requirements.txt

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Usage

1. Run the agent:

   python agent.py

2. Configure the tool as per your requirements. Detailed instructions can be found in the original repository documentation.

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Contributing

We welcome contributions! Please check the Contributing Guide for more details.

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License

This project is licensed under the MIT License. See the LICENSE file for more details.

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Acknowledgments

• The original repository: Augment SWEBench Agent
• The contributors to this fork and the SWE-Bench community for their ongoing support. main