This program is tentative and subject to change.
WebAssembly (abbreviated Wasm) has emerged as a cornerstone of web development, offering a compact binary format that allows high-performance applications to run at near-native speeds in web browsers. Despite its advantages, Wasm’s binary nature presents significant challenges for developers and researchers, particularly regarding readability when debugging or analyzing web applications. Therefore, effective decompilation becomes crucial. Unfortunately, traditional decompilers often struggle with producing readable outputs. While some large language model (LLM)-based decompilers have shown good compatibility with general binary files, they still face specific challenges when dealing with Wasm.
In this paper, we introduce a novel approach, WaDec, which is the first use of a fine-tuned LLM to interpret and decompile Wasm binary code into a higher-level, more comprehensible source code representation. The LLM was meticulously fine-tuned using a specialized dataset of wat-c code snippets, employing self-supervised learning techniques. This enables WaDec to effectively decompile not only complete wat functions but also finer-grained wat code snippets. Our experiments demonstrate that WaDec markedly outperforms current state-of-the-art tools, offering substantial improvements across several metrics. It achieves a code inflation rate of only 3.34%, a dramatic 97% reduction compared to the state-of-the-art’s 116.94%. Unlike baselines’ output that cannot be directly compiled or executed, WaDec maintains a recompilability rate of 52.11%, a re-execution rate of 43.55%, and an output consistency of 27.15%. Additionally, it significantly exceeds state-of-the-art performance in AST edit distance by 185%, cyclomatic complexity by 8%, and cosine similarity by 41%, achieving an average code similarity above 50%. In summary, WaDec enhances understanding of the code’s structure and execution flow, facilitating automated code analysis, optimization, and security auditing.
This program is tentative and subject to change.
Wed 30 OctDisplayed 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 15mTalk | 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 15mTalk | 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 15mTalk | 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 15mTalk | 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 10mTalk | LLM4Workflow: An LLM-based Automated Workflow Model Generation Tool Tool Demonstrations | ||
14:40 10mTalk | 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 10mTalk | Emergence of A Novel Domain Expert: A Generative AI-based Framework for Software Function Point Analysis NIER Track |