MAJIT: Just-in-Time Detection of Compatibility Issues in Android and iOS Apps through Large Language Model-based Multi-Agent Collaboration
This program is tentative and subject to change.
In this paper, we introduce a \textbf{\underline{M}}ulti-\textbf{\underline{A}}gent \textbf{\underline{J}}ust-\textbf{\underline{I}}n-\textbf{\underline{T}}ime detection approach: \textbf{MAJIT}, which is tailored for Android and iOS compatibility issues. Given a code change as input, our approach adopts diff-augmented analysis to provide targeted and reliable context, and further employ a scalable LLM-based multi-agent bug detection approach for compatibility issues. Our approach finally return changed code blocks predicted to have compatibility issues, along with their corresponding evidence and possible causes. We conducted our experiments on a self-collected real-world compatibility issue dataset. Experimental results show that when detecting three types of compatibility issues: system version issue, device issue, and screen size issue, our approach demonstrates over 96% on both precision and recall score.
This program is tentative and subject to change.
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 | ||
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 | ||
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 | ||