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

• URL: http://arxiv.org/abs/2603.03823v1
• Date: Wed, 04 Mar 2026 08:20:25 GMT
• Title: SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration
• Abstract summary: SWE-CI is the first repository-level benchmark built upon the Continuous Integration loop.<n>It aims to shift the evaluation paradigm for code generation from static, short-term textitfunctional correctness toward dynamic, long-term textitmaintainability
• Score: 7.89414068452646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language model (LLM)-powered agents have demonstrated strong capabilities in automating software engineering tasks such as static bug fixing, as evidenced by benchmarks like SWE-bench. However, in the real world, the development of mature software is typically predicated on complex requirement changes and long-term feature iterations -- a process that static, one-shot repair paradigms fail to capture. To bridge this gap, we propose \textbf{SWE-CI}, the first repository-level benchmark built upon the Continuous Integration loop, aiming to shift the evaluation paradigm for code generation from static, short-term \textit{functional correctness} toward dynamic, long-term \textit{maintainability}. The benchmark comprises 100 tasks, each corresponding on average to an evolution history spanning 233 days and 71 consecutive commits in a real-world code repository. SWE-CI requires agents to systematically resolve these tasks through dozens of rounds of analysis and coding iterations. SWE-CI provides valuable insights into how well agents can sustain code quality throughout long-term evolution.

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