"My productivity is boosted, but ..." Demystifying Users’ Perception on AI Coding Assistants
This program is tentative and subject to change.
This paper aims to explore fundamental questions in the era when AI coding assistants like GitHub Copilot are widely adopted: \textit{what do developers truly value and criticize in AI coding assistants, and what does this reveal about their needs and expectations in real-world software development?} Unlike previous studies that conduct observational research in controlled and simulated environments, we analyze extensive, first-hand user reviews of AI coding assistants, which capture developers’ authentic perspectives and experiences drawn directly from their actual day-to-day work contexts. We identify 1,085 AI coding assistants from the Visual Studio Code Marketplace. Although they only account for 1.64% of all extensions, we observe a surge in these assistants: over 90% of them are released within the past two years. We then manually analyze the user reviews sampled from 32 AI coding assistants that have sufficient installations and reviews to construct a comprehensive taxonomy of user concerns and feedback about these assistants. We manually annotate each review’s attitude when mentioning certain aspects of coding assistants, yielding nuanced insights into user satisfaction and dissatisfaction regarding specific features, concerns, and overall tool performance. Built on top of the findings—including how users demand not just intelligent suggestions but also context-aware, customizable, and resource-efficient interactions—we propose five practical implications and suggestions to guide the enhancement of AI coding assistants that satisfy user needs.
This program is tentative and subject to change.
Mon 17 NovDisplayed time zone: Seoul change
11:00 - 12:30 | |||
11:00 10mTalk | TensorGuard: Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification Research Papers Zehao Wu Huazhong University of Science and Technology, Yanjie Zhao Huazhong University of Science and Technology, Haoyu Wang Huazhong University of Science and Technology | ||
11:10 10mTalk | Root Cause Analysis of RISC-V Build Failures via LLM and MCTS Reasoning Research Papers Weipeng Shuai Institute of Software, Chinese Academy of Sciences, Jie Liu Institute of Software, Chinese Academy of Sciences, Zhirou Ma Institute of Software, Chinese Academy of Sciences, Liangyi Kang Institute of Software, Chinese Academy of Sciences, Zehua Wang Institute of Software, Chinese Academy of Sciences, Shuai Wang Institute of Software, Chinese Academy of Sciences, Dan Ye Institute of Software at Chinese Academy of Sciences, Hui Li , Wei Wang Institute of Software at Chinese Academy of Sciences, Jiaxin Zhu Institute of Software at Chinese Academy of Sciences | ||
11:20 10mTalk | An Empirical Study of Knowledge Transfer in AI Pair Programming Research Papers Alisa Carla Welter Saarland University, Niklas Schneider Saarland University, Tobias Dick Saarland University, Kallistos Weis Saarland University, Christof Tinnes Saarland University, Marvin Wyrich Saarland University, Sven Apel Saarland University | ||
11:30 10mTalk | Efficient Understanding of Machine Learning Model Mispredictions Research Papers Martin Eberlein Humboldt-Universtität zu Berlin, Jürgen Cito TU Wien, Lars Grunske Humboldt-Universität zu Berlin | ||
11:40 10mTalk | Can Mamba Be Better? An Experimental Evaluation of Mamba in Code Intelligence Research Papers Shuo Liu City University of Hong Kong, Jacky Keung City University of Hong Kong, Zhen Yang Shandong University, Zhenyu Mao City University of Hong Kong, Yicheng Sun City University of Hong Kong | ||
11:50 10mTalk | "My productivity is boosted, but ..." Demystifying Users’ Perception on AI Coding Assistants Research Papers | ||
12:00 10mTalk | HFUZZER: Testing Large Language Models for Package Hallucinations via Phrase-based Fuzzing Research Papers Yukai Zhao , Menghan Wu Zhejiang University, Xing Hu Zhejiang University, Xin Xia Zhejiang University | ||
12:10 10mTalk | Provable Fairness Repair for Deep Neural Networks Research Papers Jianan Ma Hangzhou Dianzi University, China; Zhejiang University, Hangzhou, China, Jingyi Wang Zhejiang University, Qi Xuan Zhejiang University of Technology; Binjiang Institute of Artificial Intelligence, Zhen Wang Hangzhou Dianzi University, China | ||
12:20 10mTalk | AutoAdapt: On the Application of AutoML for Parameter-Efficient Fine-Tuning of Pre-Trained Code Models Journal-First Track Amal Akli University of Luxembourg, Maxime Cordy University of Luxembourg, Luxembourg, Mike Papadakis University of Luxembourg, Yves Le Traon University of Luxembourg, Luxembourg | ||