EET: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents

ACL ARR 2026 January Submission4508 Authors

Keywords: Software Engineering, LLM, Agent, Efficiency

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/EffiSEAgent/EET.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: code generation and understanding

Contribution Types: Approaches to low-resource settings

Languages Studied: English, Python

Submission Number: 4508