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SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via CI

SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration

arxiv.org

March 8, 2026

2 min read

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52/100

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.

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Community Sentiment

Mixed

Positives

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.