ASE 2024
Sun 27 October - Fri 1 November 2024 Sacramento, California, United States

This program is tentative and subject to change.

Wed 30 Oct 2024 14:30 - 14:40 at Camellia - LLM for SE 2 Chair(s): Wenxi Wang

Workflows are pervasive in software systems where business processes and scientific methods are implemented as workflow models to achieve automated process execution. However, despite the benefit of no/low-code workflow automation, creating workflow models requires in-depth domain knowledge and non-trivial workflow modeling skills, which becomes a hurdle for the proliferation of workflow applications. Recently, Large language models (LLMs) have been widely applied in software code generation given their outstanding ability to understand complex instructions and generate accurate, context-aware code. Inspired by the success of LLMs in code generation, this paper aims to investigate how to use LLMs to automate workflow model generation. We present LLM4Workflow, an LLM-based automated workflow model generation tool. Using workflow descriptions as the input, LLM4Workflow can automatically embed relevant API knowledge and leverage LLM’s powerful contextual learning abilities to generate correct and executable workflow models. Its effectiveness was validated through functional verification and simulation tests on a real-world workflow system. LLM4Workflow is open sourced at https://github.com/ISEC-AHU/LLM4Workflow, and the demo video is provided at https://youtu.be/XRQ0saKkuxY.

This program is tentative and subject to change.

Wed 30 Oct

Displayed time zone: Pacific Time (US & Canada) change

13:30 - 15:00
LLM for SE 2NIER Track / Research Papers / Industry Showcase / Tool Demonstrations at Camellia
Chair(s): Wenxi Wang University of Virgina
13:30
15m
Talk
A Systematic Evaluation of Large Code Models in API Suggestion: When, Which, and How
Research Papers
Chaozheng Wang The Chinese University of Hong Kong, Shuzheng Gao Chinese University of Hong Kong, Cuiyun Gao Harbin Institute of Technology, Wenxuan Wang Chinese University of Hong Kong, Chun Yong Chong Huawei, Shan Gao Huawei, Michael Lyu The Chinese University of Hong Kong
13:45
15m
Talk
AutoDW: Automatic Data Wrangling Leveraging Large Language Models
Industry Showcase
Lei Liu Fujitsu Laboratories of America, Inc., So Hasegawa Fujitsu Research of America Inc., Shailaja Keyur Sampat Fujitsu Research of America Inc., Maria Xenochristou Fujitsu Research of America Inc., Wei-Peng Chen Fujitsu Research of America, Inc., Takashi Kato Fujitsu Research, Taisei Kakibuchi Fujitsu Research, Tatsuya Asai Fujitsu Research
14:00
15m
Talk
Instructive Code Retriever: Learn from Large Language Model's Feedback for Code Intelligence Tasks
Research Papers
jiawei lu Zhejiang University, Haoye Wang Hangzhou City University, Zhongxin Liu Zhejiang University, Keyu Liang Zhejiang University, Lingfeng Bao Zhejiang University, Xiaohu Yang Zhejiang University
14:15
15m
Talk
WaDec: Decompile WebAssembly Using Large Language Model
Research Papers
Xinyu She Huazhong University of Science and Technology, Yanjie Zhao Huazhong University of Science and Technology, Haoyu Wang Huazhong University of Science and Technology
14:30
10m
Talk
LLM4Workflow: An LLM-based Automated Workflow Model Generation Tool
Tool Demonstrations
Jia Xu Anhui University, Weilin Du Anhui University, Xiao Liu School of Information Technology, Deakin University, Xuejun Li School of Computer Science and Technology, Anhui University
14:40
10m
Talk
GPTZoo: A Large-scale Dataset of GPTs for the Research Community
NIER Track
Xinyi Hou Huazhong University of Science and Technology, Yanjie Zhao Huazhong University of Science and Technology, Shenao Wang Huazhong University of Science and Technology, Haoyu Wang Huazhong University of Science and Technology
14:50
10m
Talk
Emergence of A Novel Domain Expert: A Generative AI-based Framework for Software Function Point Analysis
NIER Track
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