SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via CI
arxiv.org
March 8, 2026
2 min read
Summary
Large language model-powered agents can automate software engineering tasks, including static bug fixing, as shown by benchmarks like SWE-bench. Real-world software development requires navigating complex requirements beyond these capabilities.
Key Takeaways
- SWE-CI is the first repository-level benchmark designed to evaluate agent capabilities in maintaining codebases through the Continuous Integration loop.
- The benchmark consists of 100 tasks with an average evolution history of 233 days and 71 consecutive commits from real-world code repositories.
- SWE-CI shifts the evaluation focus from static functional correctness to dynamic long-term maintainability of software.
- The benchmark requires agents to resolve tasks through multiple rounds of analysis and coding iterations, providing insights into sustaining code quality over time.
Community Sentiment
MixedPositives
- The benchmark of 100 tasks over 233 days provides a comprehensive evaluation of agent capabilities, which is crucial for understanding real-world performance.
- The potential to cross-reference AI-generated code with existing GitHub issues for training and validation could enhance model accuracy and relevance in practical applications.
Concerns
- Benchmarking Opus 4.6 against GPT-5.2, which is three generations behind, raises concerns about the fairness and relevance of the comparison.
- Evaluating maintainability based on only 500 lines of code changes does not adequately reflect long-term maintainability, which is essential for practical software development.
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Source
arxiv.org
Published
March 8, 2026
Reading Time
2 minutes
Relevance Score
52/100
Why It Matters
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