Setup AGent (SAG): A Dual-Model LLM Agent for Autonomous End-to-End Java Project Configuration
Environment setup remains a critical bottleneck in software development, with Java projects presenting unique challenges due to complex build tool configurations, transitive dependencies, and compilation requirements. While existing solutions like Installamatic achieve 55% success on Python repositories, Java/JVM repositories show only 29.47% success rates with current approaches.
We present SAG (Setup Agent), an LLM-based system specifically designed for Java project environment setup within Docker containers. SAG employs three key innovations: a dual-model ReAct architecture separating reasoning from action, hierarchical context management with a “Trunk/Branch” context system for complex task chains, and independent file validation ensuring objective assessment while maintaining autonomous discovery.
Through evaluation on 15 Apache projects across four model configurations, SAG achieves 78.9% overall build success and 48.4% test execution rate. The o4-mini+GPT-4.1-mini configuration achieves the highest 84.4% success using only 17,956 tokens, compared to GPT-5+GPT-5’s 75.6% success with 99,400 tokens—a 5.5× reduction in computational resources with 8.8 percentage points higher success. The extreme 12.09 thinking-to-action token ratio validates our architectural separation. Our results establish that role specialization and focused model pairing outperform uniformly applying powerful models, paving the way for efficient, task-specific automation solutions.
Fri 17 AprDisplayed time zone: Brasilia, Distrito Federal, Brazil change
16:00 - 17:30 | AI for Software Engineering 27Research Track / New Ideas and Emerging Results (NIER) at Asia IV Chair(s): Giuseppe Scanniello University of Salerno | ||
16:00 15mTalk | Setup AGent (SAG): A Dual-Model LLM Agent for Autonomous End-to-End Java Project Configuration New Ideas and Emerging Results (NIER) Chenhao Wei Stevens Institute of technology, Gengwu Zhao Stevens Institute of Technology, Xinyi Li Stevens Institute of Technology, Billy Ye Stevens Institute of Technology, Lu Xiao Stevens Institute of Technology | ||
16:15 15mTalk | MAJIT: Just-in-Time Detection of Compatibility Issues in Android and iOS Apps through Large Language Model-based Multi-Agent Collaboration New Ideas and Emerging Results (NIER) Jiaqi Wang Xidian University, Di Cui Xidian University, Shenghan Liu Douyin, Qiankang Mao Douyin, xiangxingqian Douyin, Qiaoyin Gan Douyin, Rui Li Media Attached | ||
16:30 15mTalk | Code Wars: Adversarial Self-Play for Evolving Software Validation Tools New Ideas and Emerging Results (NIER) | ||
16:45 15mTalk | SEAlign: Alignment Training for Software Engineering AgentDistinguished Paper Award Research Track Kechi Zhang Peking University, China, Huangzhao Zhang Verdent AI, Ge Li Peking University, Jinliang You Peking University, Jia Li , Yunfei Zhao Peking University, Zhi Jin Peking University, Wuhan University | ||
17:00 15mTalk | Atomizer: An LLM-based Collaborative Multi-Agent Framework for Intent-Driven Commit Untangling Research Track Kangchen Zhu National university of Defense Technology, Zhiliang Tian National University of Defense Technology, Shangwen Wang National University of Defense Technology, mingyue leng National University of Defense Technology, Xiaoguang Mao National University of Defense Technology Media Attached | ||
17:15 15mTalk | Enhancing Issue Localization Agent with Tool-Interactive Training Research Track Zexiong Ma Peking University, Chao Peng ByteDance, Qunhong Zeng Beijing Institute of Technology, Pengfei Gao ByteDance, Yanzhen Zou Peking University, Bing Xie Peking University Pre-print | ||