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Shenyu Wu
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Yunze Wu
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Zerui Peng
SJTU
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Founder's Pitch
"Building the largest open-source SWE environment for training scalable and verifiable software engineering agents."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
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4/4 signals
Series A Potential
4/4 signals
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Why It Matters
This research tackles the significant barrier of creating scalable, transparent, and verifiable environments for training software engineering agents, which can vastly enhance the capabilities and adaptability of AI-driven coding tools.
Product Angle
Productize the framework by offering a SaaS platform where companies can train and test their AI agents on a variety of curated software environments, improving their code understanding and generation capabilities.
Disruption
Disrupts existing proprietary lab environments by providing a cost-effective and transparent alternative with extensive and customizable settings for software agent training.
Product Opportunity
The market includes academia and industries focusing on AI-driven software engineering tools. Companies looking for efficient and cost-effective means to train AI models on software tasks will find this valuable.
Use Case Idea
Providing an open-source platform for developing and testing autonomous software engineering agents, facilitating research and development efficiency across academia and industry.
Science
The paper presents OpenSWE, which constructs large-scale, executable environments using Docker technology for training software engineering agents. This system incorporates a filtering pipeline to select challenging yet solvable environments for optimal learning.
Method & Eval
OpenSWE was evaluated by constructing 45,320 Docker environments from code repositories, filtering them for quality and difficulty, and using them to train models that achieved state-of-the-art performance on SWE benchmarks.
Caveats
The cost and complexity associated with maintaining such a large-scale environment are significant. Potential users must ensure compatibility with their specific use cases and prepare for handling large datasets.
Author Intelligence
Dayuan Fu
GAIR
Shenyu Wu
SJTU
Yunze Wu
SJTU
Zerui Peng
SJTU
Yaxing Huang
SJTU
Jie Sun
SII
Ji Zeng
GAIR
Mohan Jiang
GAIR
Lin Zhang
GAIR
Yukun Li
GAIR
Jiarui Hu
SII
Liming Liu
SII
Jinlong Hou
SII
Pengfei Liu
SJTU