@inproceedings{guo-etal-2026-eet,
    title = "{EET}: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents",
    author = "Guo, Yaoqi  and
      Xiao, Ying  and
      Zhang, Jie M.  and
      Harman, Mark  and
      Lou, Yiling  and
      Liu, Yang  and
      Chen, Zhenpeng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1652/",
    pages = "33008--33024",
    ISBN = "979-8-89176-395-1",
    abstract = "Software engineering (SE) agents powered by large language models are increasingly adopted in practice, yet they often incur substantial monetary cost. We introduce EET, an experience-driven early termination approach that reduces the cost of SE agents while preserving task performance. EET extracts structured experience from prior issue-resolution executions and leverages it to guide early termination during patch generation and selection, reducing unproductive iterations. We evaluate EET on the SWE-bench Verified benchmark across three representative SE agents. EET consistently reduces total cost by 19{\%}{--}55{\%} (32{\%} on average), with negligible loss in resolution rate (at most 0.2{\%}). These efficiency gains are achieved, on average, by identifying early-termination opportunities for 11{\%} of issues and reducing API calls, input tokens, and output tokens by 21{\%}, 30{\%}, and 25{\%}, respectively. We release the code, prompts, and data at https://github.com/IanWalls/EET."
}