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SWE-bench Suite(SWE-bench)
Comprehensive software engineering benchmark suite including SWE-bench, SWE-bench Verified, and SWE-bench Live for evaluating coding agents on real-world GitHub issues.
Complexity: highEvaluation and Monitoring
🎯 30-Second Overview
Pattern: Comprehensive software engineering benchmark suite evaluating LLM agents on real GitHub issues across multiple variants
Why: Industry standard for testing automated coding capabilities using 2,294+ authentic software bugs from popular Python repositories
Key Insight: Claude 3.5 Sonnet achieves 49% on Verified (500 issues) while original benchmark has 32.67% solution leakage - variant selection critical
⚡ Quick Implementation
1Setup:Install SWE-bench evaluation framework and dependencies
2Select:Choose variant: SWE-bench (2,294 issues), Verified (500), Live (1,319), or Multimodal (517)
3Configure:Set up GitHub repository access and test environment
4Evaluate:Run agent on real GitHub issues from 12+ popular Python repositories
5Assess:Measure patch generation success and functional correctness
Example: swe_bench = SWEBench(variant="verified", repos=["django", "flask"], model=coding_agent, timeout=1800)
📋 Do's & Don'ts
✅Use SWE-bench Verified (500 issues) for reliable human-validated evaluation
✅Test on multiple repository types (Django, Flask, Requests, Matplotlib, etc.)
✅Monitor for data contamination - many issues predate model training cutoffs
✅Include multimodal variant (517 issues) for visual debugging tasks
✅Use SWE-bench Live for contamination-free evaluation with post-2024 issues
❌Rely solely on original SWE-bench - 32.67% have solution leakage issues
❌Ignore patch correctness validation - weak test cases affect 31% of results
❌Skip repository-specific setup requirements and dependency management
❌Assume high scores without validating actual problem-solving capability
❌Overlook visual elements in multimodal issues requiring image interpretation
🚦 When to Use
Use When
- • Evaluating coding agents on real-world software engineering tasks
- • Benchmarking GitHub issue resolution capabilities
- • Testing automated patch generation and bug fixing
- • Assessing multi-repository code understanding
- • Research on LLM software engineering performance
Avoid When
- • Simple coding task evaluation (use HumanEval instead)
- • Non-Python programming language assessment
- • Rapid prototyping evaluation without full repository context
- • Educational coding exercises rather than real bugs
- • Time-sensitive evaluations (can take hours per issue)
📊 Key Metrics
% Resolved (Primary)
Percentage of GitHub issues successfully resolved with working patches
Pass@k Success Rate
Success rate across k attempts (typically k=1)
Functional Correctness
Patches that actually fix the issue without breaking existing tests
Repository Coverage
Performance across 12+ diverse Python repositories
Solution Quality
Patch elegance, maintainability, and adherence to coding standards
Time to Resolution
Average time taken to generate working patches
💡 Top Use Cases
Industry Standard Evaluation: Primary benchmark for coding LLMs like Claude 3.5 Sonnet (49% on Verified)
Academic Research: Princeton/CMU framework for LLM software engineering capability assessment
Production Readiness Testing: Validating AI coding assistants on real GitHub issues before deployment
Automated Debugging: Testing AI agents' ability to understand, analyze, and fix complex software bugs
Multi-Repository Assessment: Evaluating code understanding across Django, Flask, Matplotlib, Requests, and more
References & Further Reading
Deepen your understanding with these curated resources
Official Leaderboards & Tools
Contribute to this collection
Know a great resource? Submit a pull request to add it.
💻
SWE-bench Suite(SWE-bench)
Comprehensive software engineering benchmark suite including SWE-bench, SWE-bench Verified, and SWE-bench Live for evaluating coding agents on real-world GitHub issues.
Complexity: highEvaluation and Monitoring
🎯 30-Second Overview
Pattern: Comprehensive software engineering benchmark suite evaluating LLM agents on real GitHub issues across multiple variants
Why: Industry standard for testing automated coding capabilities using 2,294+ authentic software bugs from popular Python repositories
Key Insight: Claude 3.5 Sonnet achieves 49% on Verified (500 issues) while original benchmark has 32.67% solution leakage - variant selection critical
⚡ Quick Implementation
1Setup:Install SWE-bench evaluation framework and dependencies
2Select:Choose variant: SWE-bench (2,294 issues), Verified (500), Live (1,319), or Multimodal (517)
3Configure:Set up GitHub repository access and test environment
4Evaluate:Run agent on real GitHub issues from 12+ popular Python repositories
5Assess:Measure patch generation success and functional correctness
Example: swe_bench = SWEBench(variant="verified", repos=["django", "flask"], model=coding_agent, timeout=1800)
📋 Do's & Don'ts
✅Use SWE-bench Verified (500 issues) for reliable human-validated evaluation
✅Test on multiple repository types (Django, Flask, Requests, Matplotlib, etc.)
✅Monitor for data contamination - many issues predate model training cutoffs
✅Include multimodal variant (517 issues) for visual debugging tasks
✅Use SWE-bench Live for contamination-free evaluation with post-2024 issues
❌Rely solely on original SWE-bench - 32.67% have solution leakage issues
❌Ignore patch correctness validation - weak test cases affect 31% of results
❌Skip repository-specific setup requirements and dependency management
❌Assume high scores without validating actual problem-solving capability
❌Overlook visual elements in multimodal issues requiring image interpretation
🚦 When to Use
Use When
- • Evaluating coding agents on real-world software engineering tasks
- • Benchmarking GitHub issue resolution capabilities
- • Testing automated patch generation and bug fixing
- • Assessing multi-repository code understanding
- • Research on LLM software engineering performance
Avoid When
- • Simple coding task evaluation (use HumanEval instead)
- • Non-Python programming language assessment
- • Rapid prototyping evaluation without full repository context
- • Educational coding exercises rather than real bugs
- • Time-sensitive evaluations (can take hours per issue)
📊 Key Metrics
% Resolved (Primary)
Percentage of GitHub issues successfully resolved with working patches
Pass@k Success Rate
Success rate across k attempts (typically k=1)
Functional Correctness
Patches that actually fix the issue without breaking existing tests
Repository Coverage
Performance across 12+ diverse Python repositories
Solution Quality
Patch elegance, maintainability, and adherence to coding standards
Time to Resolution
Average time taken to generate working patches
💡 Top Use Cases
Industry Standard Evaluation: Primary benchmark for coding LLMs like Claude 3.5 Sonnet (49% on Verified)
Academic Research: Princeton/CMU framework for LLM software engineering capability assessment
Production Readiness Testing: Validating AI coding assistants on real GitHub issues before deployment
Automated Debugging: Testing AI agents' ability to understand, analyze, and fix complex software bugs
Multi-Repository Assessment: Evaluating code understanding across Django, Flask, Matplotlib, Requests, and more
References & Further Reading
Deepen your understanding with these curated resources
Official Leaderboards & Tools
Contribute to this collection
Know a great resource? Submit a pull request to add it.