SWE-bench
BenchmarkFreeReal-world software engineering task evaluation suite
Capabilities3 decomposed
real-world bug detection evaluation
Medium confidenceSWE-bench evaluates AI systems by testing their ability to locate bugs in real-world codebases sourced from GitHub issues. It utilizes a dataset of actual software engineering tasks, which allows for more realistic assessments compared to synthetic benchmarks like HumanEval. The evaluation framework is designed to simulate real-world scenarios, ensuring that models are tested against practical challenges faced by developers.
SWE-bench's unique approach lies in its use of real-world GitHub issues, providing a more authentic evaluation of AI capabilities compared to purely synthetic benchmarks.
More comprehensive than HumanEval as it tests against actual software engineering tasks rather than contrived examples.
automated fix writing evaluation
Medium confidenceThis capability assesses the ability of AI models to generate fixes for identified bugs within real codebases. SWE-bench evaluates how well models can not only detect issues but also propose appropriate code modifications. The evaluation framework includes a variety of bug types and contexts, ensuring that the models are tested against a wide range of scenarios that developers encounter in practice.
SWE-bench uniquely combines bug detection and fix generation in its evaluation, allowing for a comprehensive assessment of AI capabilities in real-world scenarios.
More holistic than other benchmarks, as it evaluates both bug detection and the subsequent fix generation in a single framework.
test suite passing evaluation
Medium confidenceSWE-bench evaluates whether AI-generated fixes can pass existing test suites in real codebases. This capability ensures that the proposed solutions not only address the bugs but also maintain the integrity of the software by passing all relevant tests. The evaluation framework integrates with various testing frameworks to verify that the code modifications do not introduce new issues.
SWE-bench's integration with existing test suites allows for a rigorous evaluation of AI-generated fixes, ensuring that they meet real-world quality standards.
Offers a more thorough validation process than other benchmarks by ensuring that fixes not only address bugs but also pass all relevant tests.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with SWE-bench, ranked by overlap. Discovered automatically through the match graph.
UseTusk
AI-powered tool for automated bug detection and smart...
Ellipsis
(Previously BitBuilder) "Automated code reviews and bug fixes"
Safurai
Transform the way you use ChatGPT for...
SourceAI
AI-driven coding tool, quick, intuitive, for all...
Factory
Revolutionize software development with autonomous AI-driven...
GitHub Copilot X
AI-powered software developer
Best For
- ✓AI researchers developing bug detection models
- ✓developers testing autonomous coding agents
- ✓developers creating AI-assisted coding tools
- ✓researchers focused on code generation
- ✓QA engineers testing AI-generated code
- ✓developers ensuring code quality
Known Limitations
- ⚠Limited to the scope of tasks available in the dataset, which may not cover all programming languages or frameworks
- ⚠Dependent on the quality and variety of the GitHub issues included
- ⚠Evaluation may not cover all programming languages or frameworks present in the dataset
- ⚠Fixes generated may require manual review for correctness
- ⚠Dependent on the availability and comprehensiveness of the test suites in the dataset
- ⚠May not cover all edge cases present in real-world applications
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.
About
SWE-bench evaluates AI systems on real software engineering tasks extracted from GitHub issues. Tests whether models can locate bugs, write fixes, and pass existing test suites in real codebases. More realistic than HumanEval because it uses actual open-source projects. Benchmark for autonomous coding agents (Devin, AutoFix, etc.). Princeton's production-grade evaluation suite.
Categories
Alternatives to SWE-bench
Are you the builder of SWE-bench?
Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.
Get the weekly brief
New tools, rising stars, and what's actually worth your time. No spam.
Data Sources
Looking for something else?
Search →