Decide: Knowledge-based Version Incompatibility Detection in Deep Learning Stacks
Version incompatibility issues are prevalent when reusing or reproducing deep learning (DL) models and applications. Compared with official API documentation, which is often incomplete or out-of-date, Stack Overflow (SO) discussions possess a wealth of version knowledge that has not been explored by previous approaches. To bridge this gap, we present Decide, a web-based visualization of a knowledge graph that contains 2,376 version knowledge extracted from SO discussions. As an interactive tool, Decide allows users to easily check whether two libraries are compatible or not and explore compatibility knowledge of certain DL stack components with or without the version specified. A video demonstrating the usage of Decide is available at https://youtu.be/wqPxF2ZaZo0.
Thu 18 JulDisplayed time zone: Brasilia, Distrito Federal, Brazil change
16:00 - 18:00 | SE4AI 2Research Papers / Industry Papers / Demonstrations / Journal First at Mandacaru Chair(s): Wei Yang University of Texas at Dallas | ||
16:00 18mTalk | Natural Is The Best: Model-Agnostic Code Simplification for Pre-trained Large Language Models Research Papers Yan Wang Central University of Finance and Economics, Xiaoning Li Central University of Finance and Economics, Tien N. Nguyen University of Texas at Dallas, Shaohua Wang Central University of Finance and Economics, Chao Ni School of Software Technology, Zhejiang University, Ling Ding Central University of Finance and Economics Pre-print Media Attached File Attached | ||
16:18 18mTalk | On Reducing Undesirable Behavior in Deep-Reinforcement-Learning-Based Software Research Papers | ||
16:36 9mTalk | Decide: Knowledge-based Version Incompatibility Detection in Deep Learning Stacks Demonstrations Zihan Zhou The University of Hong Kong, Zhongkai Zhao National University of Singapore, Bonan Kou Purdue University, Tianyi Zhang Purdue University DOI Pre-print Media Attached | ||
16:45 18mTalk | Test input prioritization for Machine Learning Classifiers Journal First Xueqi Dang University of Luxembourg, Yinghua LI University of Luxembourg, Mike Papadakis University of Luxembourg, Jacques Klein University of Luxembourg, Tegawendé F. Bissyandé University of Luxembourg, Yves Le Traon University of Luxembourg, Luxembourg | ||
17:03 18mTalk | How Far Are We with Automated Machine Learning? Characterization and Challenges of AutoML Toolkits Journal First | ||
17:21 18mTalk | Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4 Industry Papers Xuchao Zhang Microsoft, Supriyo Ghosh Microsoft, Chetan Bansal Microsoft Research, Rujia Wang Microsoft, Minghua Ma Microsoft Research, Yu Kang Microsoft Research, Saravan Rajmohan Microsoft | ||
17:39 18mTalk | Exploring LLM-based Agents for Root Cause Analysis Industry Papers Devjeet Roy Washington State University, Xuchao Zhang Microsoft, Rashi Bhave Microsoft Research, Chetan Bansal Microsoft Research, Pedro Las-Casas Microsoft, Rodrigo Fonseca Microsoft Research, Saravan Rajmohan Microsoft | ||