Hugging Face Releases CrossSWE: Benchmark for Cross-Repository Collaborative Development
Summary:Hugging Face has released CrossSWE, a benchmark designed to evaluate AI agents in cross-repository collaborative development tasks. The benchmark includes 171 real-world instances spanning 165 repositories, 20 technical domains, and 13 programming languages. Evaluations of five LLMs, including the strongest model GPT-6.1 Sol, revealed significant challenges, with a pass rate of only 58.48% and cross-repository inconsistencies emerging as the predominant failure mode (44.2%). This release undersc
Background and Challenges
As AI agents demonstrate increasingly high success rates in software engineering tasks, the complexity of these tasks has become a new challenge. Traditional benchmarks primarily focus on tasks within a single repository, while real-world software development often involves multiple interdependent repositories, introducing additional challenges such as dependency reasoning, change propagation, and interface consistency.
The Release of CrossSWE
Hugging Face's CrossSWE aims to address this gap. The benchmark includes 171 real-world instances spanning 165 repositories, 20 technical domains, and 13 programming languages. Evaluations of five LLMs, including the strongest model GPT-6.1 Sol, revealed significant challenges:
- Cross-Repository Inconsistencies: This is the predominant failure mode, accounting for 44.2% of failures.
- Performance Bottlenecks: The best-performing model, GPT-6.1 Sol, achieved only 58.48% in the Pass@1 metric.
Technical Mechanism Analysis
CrossSWE simulates real development scenarios through the following:
- Multi-Repository Integration: Testing AI agents' ability to coordinate across multiple repositories.
- Dependency Reasoning: Evaluating agents' performance in handling complex dependency relationships.
- Change Propagation: Testing agents' ability to assess the impact of code changes on related modules.
Engineering Trade-offs and Performance
- Strengths: CrossSWE provides a highly complex testing environment that more accurately reflects the real-world application scenarios of AI in software engineering.
- Challenges: Cross-repository collaboration tasks place higher demands on AI agents' dependency reasoning and consistency maintenance capabilities, and the performance of existing models still has significant room for improvement.
Developer Implementation and Deployment Recommendations
- Model Optimization: Developers should focus on dependency reasoning and consistency maintenance in cross-repository collaboration, optimizing model architecture to improve performance.
- Data Augmentation: Utilize the real-world scenario data provided by CrossSWE to further train and fine-tune models.
- Continuous Monitoring: During deployment, continuously monitor AI agents' performance in cross-repository collaboration tasks and adjust strategies as needed.
Future Outlook
The release of CrossSWE provides a new benchmark tool for the application of AI in software engineering, helping to drive the performance improvement of AI agents in complex tasks. It also provides important references for the development of future AI-driven software development tools.
— END —Source: Hugging Face Daily Papers (2026-11-01)
Tags: #Hugging Face #CrossSWE #AI Agents #Software Engineering #Cross-Repository Collaboration
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