THUDM/SWE-Dev-train

Dataset

πŸš€ SWE-Dev, an open-source Agent for Software Engineering tasks! This repository contains the SWE-Dev-32B model as presented in the paper SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling.

22

14 commits

1 linked in READMEs

updated Jul 9, 2025

See the code

README

πŸ“ Paper | 🌐 Github

πŸš€ SWE-Dev, an open-source Agent for Software Engineering tasks! This repository contains the SWE-Dev-32B model as presented in the paper SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling.

πŸ’‘ We develop a comprehensive pipeline for creating developer-oriented datasets from GitHub repositories, including issue tracking, code localization, test case generation, and evaluation.

πŸ”§ Based on open-source frameworks (OpenHands) and models, SWE-Dev-7B and 32B achieved solve rates of 23.4% and 36.6% on SWE-bench-Verified, respectively, even approaching the performance of GPT-4o.

πŸ“š We find that training data scaling and inference scaling can both effectively boost the performance of models on SWE-bench. Moreover, higher data quality further improves this trend when combined with reinforcement fine-tuning (RFT). For inference scaling specifically, the solve rate on SWE-Dev increased from 34.0% at 30 rounds to 36.6% at 75 rounds.

Contributors

hrw

14 commits

THUDM/SWE-Dev-train

Dataset

πŸš€ SWE-Dev, an open-source Agent for Software Engineering tasks! This repository contains the SWE-Dev-32B model as presented in the paper SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling.

22

14 commits

1 linked in READMEs

updated Jul 9, 2025

See the code

README

πŸ“ Paper | 🌐 Github

πŸš€ SWE-Dev, an open-source Agent for Software Engineering tasks! This repository contains the SWE-Dev-32B model as presented in the paper SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling.

πŸ’‘ We develop a comprehensive pipeline for creating developer-oriented datasets from GitHub repositories, including issue tracking, code localization, test case generation, and evaluation.

πŸ”§ Based on open-source frameworks (OpenHands) and models, SWE-Dev-7B and 32B achieved solve rates of 23.4% and 36.6% on SWE-bench-Verified, respectively, even approaching the performance of GPT-4o.

πŸ“š We find that training data scaling and inference scaling can both effectively boost the performance of models on SWE-bench. Moreover, higher data quality further improves this trend when combined with reinforcement fine-tuning (RFT). For inference scaling specifically, the solve rate on SWE-Dev increased from 34.0% at 30 rounds to 36.6% at 75 rounds.

Contributors

hrw

14 commits