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SWE-Bench Coding Tasks Dataset

SWE-Bench Coding Tasks Dataset  is an extended programming languages dataset that builds on the original SWE-Bench benchmark with broader language coverage, golden/test patches, and real-world coding tasks like 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.

Get in touch Download sample
  • files
    8,712
  • programming languages
    6
Fermatix SWE-Bench dataset
  • Programming languages
  • Machine Learning
  • Automated Code Review
  • Bug Fixing

SWE-Bench Coding Tasks Dataset  is an extended programming languages dataset that builds on the original SWE-Bench benchmark with broader language coverage, golden/test patches, and real-world coding tasks like 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.

Get in touch Download sample
  • 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
Fermatix SWE-Bench dataset
Download sample

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
Source and collection methodology. Data was collected from permissively-licensed, non-utilitarian GitHub repositories to ensure diversity and reduce bias.

Dataset Use Cases

  • Software Development

    Improving Automated Code Generation

    SWE-Bench Coding Tasks Dataset is an extended programming languages dataset that builds on the original SWE-Bench benchmark with broader language coverage, golden/test patches, and real-world coding tasks like 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. provides structured test sets from real GitHub issues, enabling evaluation of coding agents on advanced coding tasks. By offering a programming languages dataset with real-world repositories, it supports developers in benchmarking pass rates, analyzing coding benchmarks, and improving accuracy in code generation and bug fixing.

  • Machine Learning & AI

    Training and Evaluating Coding Agents

    This SWE-bench verified dataset helps researchers train large language models on software engineering challenges. With tasks requiring Python repositories and large codebases, the Multi-SWE-Bench framework delivers benchmark scores and evaluation results, making it a reliable resource for building better coding models and testing new benchmarks in real software environments.

  • Software Engineering Research

    Benchmarking Real-World Engineering Tasks

    SWE-Bench Dataset introduces a robust SWE benchmark for analyzing engineering tasks across large codebases. By including GitHub repository issues and patches, it creates realistic conditions for testing developer tools, assessing coding tasks, and validating language models against existing benchmarks, enhancing reliability in software engineering research and development practices.

  • Developer Tools & Testing

    Enhancing Reliability in Software Projects

    With nearly 9,000 files and curated annotations, this dataset helps improve developer tools for bug fixing and pull request generation. It supports testing coding agents in python projects, refining evaluation results, and addressing real-world coding challenges, strengthening recognition of pass rates across software development and testing pipelines.

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.
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