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/EffiSEAgent/EET.

翻译：基于大语言模型的软件工程智能体在实践中日益普及，但其往往产生高昂的货币成本。本文提出EET——一种基于经验驱动的早期终止方法，在保持任务性能的同时降低软件工程智能体的运行成本。EET从历史问题解决记录中提取结构化经验，并利用该经验在补丁生成与选择阶段引导早期终止，从而减少无效迭代。我们在SWE-bench Verified基准测试中针对三种代表性软件工程智能体评估EET。该方法能持续降低总成本的19%-55%（平均32%），而问题解决率损失可忽略不计（最多0.2%）。这些效率提升平均通过识别11%问题的早期终止机会实现，并使API调用、输入令牌和输出令牌分别减少21%、30%和25%。我们在https://github.com/EffiSEAgent/EET公开了代码、提示词及数据。