SWE-bench_Verified
DatasetFreeDataset by princeton-nlp. 6,78,148 downloads.
Capabilities5 decomposed
verified-software-engineering-task-dataset-loading
Medium confidenceLoads a curated dataset of 500 real GitHub issues paired with their ground-truth solutions, verified through human review and automated validation. The dataset is distributed in Parquet format optimized for streaming and batch processing, with built-in support for HuggingFace Datasets, Pandas, Polars, and MLCroissant libraries. Each record contains issue description, repository context, and verified fix code, enabling direct evaluation of code generation models on authentic software engineering tasks.
Combines human verification with automated validation to ensure ground-truth correctness — each fix is reviewed by domain experts and tested against original issue reproduction steps, unlike crowd-sourced datasets that rely solely on majority voting or automated heuristics
More reliable than CodeSearchNet or GitHub-sourced datasets because verification eliminates incorrect or partial solutions, and more representative than synthetic benchmarks because tasks are extracted from real production issues with authentic complexity and edge cases
multi-format-dataset-export-and-conversion
Medium confidenceExports verified task records from HuggingFace Hub to multiple serialization formats (Parquet, CSV, Arrow, JSON) with automatic schema preservation and type inference. Supports streaming export for large datasets and batch conversion pipelines using Pandas, Polars, or MLCroissant metadata standards. Enables seamless integration with downstream analysis tools, ML frameworks, and data warehouses without manual schema mapping.
Supports MLCroissant metadata generation alongside data export, enabling automatic dataset discovery and FAIR compliance — most benchmark datasets only provide raw data without machine-readable provenance, licensing, or schema documentation
More flexible than direct HuggingFace Hub downloads because it enables format conversion and filtering at export time, reducing post-processing overhead compared to downloading full Parquet and manually converting in separate scripts
benchmark-task-filtering-and-stratification
Medium confidenceFilters and stratifies the 500 verified tasks by repository characteristics (language, size, test coverage), issue properties (complexity, category), and solution properties (lines changed, test pass rate) using declarative query syntax. Enables creation of balanced evaluation subsets for targeted model assessment — e.g., isolating tasks requiring specific capabilities or controlling for dataset bias. Supports both eager filtering (in-memory) and lazy evaluation (deferred computation) for memory-efficient processing.
Supports lazy evaluation through Polars and Arrow backends, enabling memory-efficient filtering of large stratified subsets without materializing intermediate results — most benchmark tools require eager filtering that loads entire dataset into memory
More flexible than static benchmark splits because filtering is declarative and composable, allowing researchers to create custom evaluation sets on-the-fly rather than being limited to predefined train/test/validation partitions
ground-truth-solution-validation-and-reproducibility
Medium confidenceProvides verified ground-truth solutions for each task with reproducible validation — each fix includes the exact test commands, expected outputs, and commit hashes needed to reproduce the solution in the original repository context. Enables deterministic evaluation by specifying exact Python versions, dependency versions, and environment configurations. Validation is performed through automated test execution against the original issue reproduction steps, ensuring solutions actually resolve the reported problem.
Includes exact test commands and commit hashes for reproducible validation in original repository context, unlike synthetic benchmarks that provide only expected outputs without ability to re-run tests in authentic development environments
More rigorous than string-matching evaluation because it validates fixes by executing actual test suites, catching semantic errors and edge cases that string similarity metrics would miss
model-evaluation-harness-integration
Medium confidenceProvides standardized interfaces for integrating the benchmark into model evaluation pipelines, with built-in support for popular frameworks (HuggingFace Transformers, LangChain, LLaMA Index). Includes evaluation metrics (pass@k, exact match, test pass rate) and utilities for logging results to experiment tracking systems (Weights & Biases, MLflow). Enables end-to-end evaluation workflows from model inference through result aggregation and comparison.
Provides standardized evaluation interfaces compatible with HuggingFace Transformers and LangChain ecosystems, enabling plug-and-play integration with existing model evaluation infrastructure rather than requiring custom evaluation scripts
More integrated than manual evaluation because it automates metric computation and experiment logging, reducing boilerplate code and enabling reproducible benchmarking across teams and environments
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓ML researchers evaluating code generation and software engineering AI models
- ✓Teams building and fine-tuning LLM agents for autonomous code repair and issue resolution
- ✓Benchmark maintainers and dataset curators validating model performance on real-world tasks
- ✓Data engineers building ETL pipelines that consume benchmark data from multiple sources
- ✓Researchers sharing datasets with collaborators using different tools (Excel, R, SQL databases)
- ✓ML practitioners integrating benchmarks into existing PyTorch/TensorFlow training pipelines
- ✓ML researchers conducting controlled ablation studies and capability analysis of code generation models
- ✓Teams evaluating model performance on specific domains (e.g., only web framework issues or security-critical fixes)
Known Limitations
- ⚠Dataset size is fixed at 500 verified instances — insufficient for training large models from scratch, primarily designed for evaluation
- ⚠Verification process is human-in-the-loop, introducing potential annotation bias and limiting scalability to new domains
- ⚠All tasks are GitHub-sourced Python repositories — limited coverage of other languages, frameworks, and non-open-source codebases
- ⚠No built-in temporal versioning — snapshot represents specific point in time, may not reflect evolving best practices or security patches
- ⚠CSV export loses nested structure — complex fields (test patches, code diffs) require custom serialization logic
- ⚠No built-in compression for export — CSV/JSON files are 3-5x larger than Parquet, increasing storage and transfer costs
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
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SWE-bench_Verified — a dataset on HuggingFace with 6,78,148 downloads
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