SWE-smith: Scaling Data for Software Engineering Agents
John Yang, Kilian Leret, Carlos E. Jimenez, Alexander Wettig, Kabir Khandpur, Yanzhe Zhang, Binyuan Hui, Ofir Press, Ludwig Schmidt, Diyi Yang
2025-05-07
Summary
This paper talks about SWE-smith, a system that automatically creates huge amounts of training data to help AI get better at software engineering tasks like coding and debugging.
What's the problem?
AI needs a lot of high-quality examples to learn how to do complex software engineering jobs, but collecting and preparing this data by hand is slow, expensive, and often not enough for really advanced training.
What's the solution?
The researchers developed SWE-smith to automatically generate and organize large datasets, which helps language models learn faster and perform better on tasks like writing code or finding bugs.
Why it matters?
This matters because it means AI tools for software development can become smarter and more helpful, making programming easier and more efficient for everyone, from students to professional developers.
Abstract
SWE-smith automates the generation of large-scale software engineering training data and improves the performance of language models on automated software engineering tasks.