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

[2603.03823] SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration Support arXiv on Cornell Giving Day! We're celebrating 35 years of open science - with YOUR support! Your generosity has helped arXiv thrive for three and a half decades. Give today to help keep science open for ALL for many years to come. Donate! --> Computer Science > Software Engineering arXiv:2603.03823 (cs) [Submitted on 4 Mar 2026] Title: SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration Authors: Jialong Chen , Xander Xu , Hu Wei , Chuan Chen , Bing Zhao View a PDF of the paper titled SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration, by Jialong Chen and 4 other authors View PDF HTML (experimental) 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. Subjects: Software Engineering (cs.SE) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2603.03823 [cs.SE] (or arXiv:2603.03823v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2603.03823 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jialong Chen [ view email ] [v1] Wed, 4 Mar 2026 08:20:25 UTC (3,311 KB) Full-text links: Access Paper: View a PDF of the paper titled SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration, by Jialong Chen and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI cs.CL References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )