SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering?
February 17, 2025 ยท Declared Dead ยท ๐ International Conference on Machine Learning
Repo contents: README.md
Authors
Samuel Miserendino, Michele Wang, Tejal Patwardhan, Johannes Heidecke
arXiv ID
2502.12115
Category
cs.LG: Machine Learning
Cross-listed
cs.SE
Citations
67
Venue
International Conference on Machine Learning
Repository
https://github.com/openai/SWELancer-Benchmark
โญ 1439
Last Checked
1 month ago
Abstract
We introduce SWE-Lancer, a benchmark of over 1,400 freelance software engineering tasks from Upwork, valued at \$1 million USD total in real-world payouts. SWE-Lancer encompasses both independent engineering tasks--ranging from \$50 bug fixes to \$32,000 feature implementations--and managerial tasks, where models choose between technical implementation proposals. Independent tasks are graded with end-to-end tests triple-verified by experienced software engineers, while managerial decisions are assessed against the choices of the original hired engineering managers. We evaluate model performance and find that frontier models are still unable to solve the majority of tasks. To facilitate future research, we open-source a unified Docker image and a public evaluation split, SWE-Lancer Diamond (https://github.com/openai/SWELancer-Benchmark). By mapping model performance to monetary value, we hope SWE-Lancer enables greater research into the economic impact of AI model development.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Machine Learning
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
R.I.P.
๐ป
Ghosted
Semi-Supervised Classification with Graph Convolutional Networks
R.I.P.
๐ป
Ghosted
Proximal Policy Optimization Algorithms
R.I.P.
๐ป
Ghosted
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Died the same way โ ๐ Death by README
R.I.P.
๐
Death by README
Momentum Contrast for Unsupervised Visual Representation Learning
R.I.P.
๐
Death by README
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
R.I.P.
๐
Death by README
Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
R.I.P.
๐
Death by README