@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://aclanthology.org/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."
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%0 Conference Proceedings
%T EET: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents
%A Guo, Yaoqi
%A Xiao, Ying
%A Zhang, Jie M.
%A Harman, Mark
%A Lou, Yiling
%A Liu, Yang
%A Chen, Zhenpeng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F guo-etal-2026-eet
%X 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.
%U https://aclanthology.org/2026.findings-acl.1652/
%P 33008-33024
Markdown (Informal)
[EET: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents](https://aclanthology.org/2026.findings-acl.1652/) (Guo et al., Findings 2026)
ACL
- Yaoqi Guo, Ying Xiao, Jie M. Zhang, Mark Harman, Yiling Lou, Yang Liu, and Zhenpeng Chen. 2026. EET: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents. In Findings of the Association for Computational Linguistics: ACL 2026, pages 33008–33024, San Diego, California, United States. Association for Computational Linguistics.