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Commercial
SWE-Bench Coding Tasks Dataset
Extended programming languages dataset based on SWE-Bench, featuring broader language coverage, golden and test patches, and real-world coding tasks such as bug fixing, code completion, and automated code review. It supports coding agents, language models, and developer tools with verified benchmark scores and multi-language test sets.
-
- files
- 8,712
-
- programming languages
- 6
- Programming languages
- Machine Learning
- Automated Code Review
- Bug Fixing
- Programming languages
- Machine Learning
- Automated Code Review
- Bug Fixing
Extended programming languages dataset based on SWE-Bench, featuring broader language coverage, golden and test patches, and real-world coding tasks such as bug fixing, code completion, and automated code review. It supports coding agents, language models, and developer tools with verified benchmark scores and multi-language test sets.
- Programming languages
- Machine Learning
- Automated Code Review
- Bug Fixing
-
- files
- 8,712
-
- programming languages
- 6
Dataset Info
| Characteristic | Data |
| Description | An extended benchmark of real-world software engineering tasks with enhanced artifacts and broader language coverage |
| Data types | Text |
| Tasks | Bug fixing, code completion, pull request generation, automated code review |
| Total number of files | 8,712 |
| Total number of people | 30 |
| Labeling | Annotated with golden patches, test patches, post-patch reference states, and metadata stored in parquet files (e.g., repository name, issue/PR identifier, diffs, test results) |
| Programming languages | C#, Go, PHP, Rust, Kotlin, Ruby |
Technical
Characteristics
| Characteristic | Data |
| Files Extensions | parquet (metadata), .patch (golden/test patches), .txt/.xml (reference outputs), .yml (docker-compose), Dockerfile, Makefile, .env |
| Models | Compatible with original Multi-SWE-Bench execution tools and models designed for code understanding and generation |
| File Size | 8.85 GB |
Dataset Use Cases
FAQs
What makes SWE-Bench Coding Tasks Dataset different from existing benchmarks?
Unlike existing benchmarks limited to one language, this one expands to multiple languages and provides enhanced metadata. This makes it a more robust option for advanced coding evaluation, engineering tasks, and software development research.
Which programming languages are supported?
The dataset covers multiple programming languages including Python, C#, Go, PHP, Rust, Kotlin, and Ruby. This broader scope makes it suitable for multilingual coding benchmarks and large codebases.
How large is the dataset and in what format is it available?
The dataset size is 8.85 GB, with files in .parquet, .patch, .yml, .txt, .xml, along with Dockerfiles and Makefiles. This structure ensures compatibility with Multi-SWE-Bench execution tools and reproducible workflows.
What types of annotations are provided?
Annotations include golden patches, test patches, diffs, test results, and repository metadata. This ensures models are evaluated against verified coding benchmarks with clear pass/fail criteria, supporting transparent evaluation results.
Do Unidata datasets follow GDPR and other privacy regulations?
Yes. All Unidata datasets comply with GDPR and applicable international privacy laws. Data is collected from legally permissible sources and curated to ensure ethical and responsible usage.
How are Unidata datasets stored?
All datasets are stored securely on AWS cloud infrastructure, following ISO 27001 and ISO 27701 standards. This ensures high security, availability, and reliable long-term dataset management.
Is this dataset real-world or synthetic?
This is a real-world dataset, sourced from genuine GitHub issues, pull requests, and repository histories. Each task reflects actual engineering challenges encountered in live software projects.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model. Free samples are available for testing and trial use, while full datasets are provided exclusively through purchase for commercial or large-scale research applications.
Why is SWE-Bench useful for evaluating coding LLMs?
Unlike synthetic programming benchmarks, SWE-Bench measures whether large language models can solve realistic software engineering problems using existing project codebases, dependencies, and repository context.
Still have questions about using Unidata datasets?
Read our user-guides
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