EET: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents
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.
Cite
@article{arxiv.2601.05777,
title = {EET: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents},
author = {Yaoqi Guo and Ying Xiao and Jie M. Zhang and Mark Harman and Yiling Lou and Yang Liu and Zhenpeng Chen},
journal= {arXiv preprint arXiv:2601.05777},
year = {2026}
}
Comments
Accepted by the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) Findings Track