SWE-bench — LLM Evaluation & Benchmarks Dataset
2,294 real-world GitHub issues and their verified patches from 12 popular Python repositories. The gold standard benchmark for evaluating LLMs on practical software engineering tasks — writing actual code that resolves real bugs in production codebases.
Dataset Details
| Provider | princeton-nlp |
| Category | Evaluation & Benchmarks |
| Size | 2,294 Tasks |
| License | MIT |
| Downloads | 450k |
| Tags | Software Engineering, Real-world, GitHub, Benchmark, Code |
from datasets import load_dataset
ds = load_dataset("princeton-nlp/SWE-bench")
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Frequently asked questions
Can I use SWE-bench commercially?
Yes — SWE-bench is released under MIT, a permissive license that allows commercial use, including training models you ship in a product. Check the dataset card for attribution requirements before release.
How much data does SWE-bench contain, and do I need all of it?
SWE-bench contains 2,294 Tasks. It is an evaluation benchmark, so it is used in full to measure models — never mix it into training data, or your benchmark scores become meaningless.
What is SWE-bench best used for?
Benchmarking real-world software engineering - never train on it. It belongs to the Evaluation & Benchmarks section of our dataset hub, where you'll find alternatives and complementary sets.
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