PseudoFix: Refactoring Distorted Structures in Decompiled C Pseudocode
This program is tentative and subject to change.
Decompilation can convert binary programs into clear C-style pseudocode, which is of great value in a wide range of security applications. Existing research primarily focuses on recovering symbolic information in pseudocode, such as function names, variable names, and data types, but neglecting structural information. We observe that even when symbolic information is fully preserved, severe and complex structure distortions remain in the pseudocode, greatly impairing code readability and comprehension. In this work, we first systematically investigate structure distortions in decompiled pseudocode, revealing their variation patterns through quantitative analysis. Using open coding, we derive a taxonomy comprising six top-level categories of structure distortions. Building upon this taxonomy, we propose PseudoFix, a novel framework that combines large language models (LLMs) with retrieval-based in-context learning. PseudoFix employs semantic retrieval to select the most relevant few-shot examples that provide structure distortion knowledge, and combines this with the well-structured coding patterns learned by LLMs from vast source code repositories, to efficiently refactor distorted pseudocode. Comprehensive evaluations demonstrate that PseudoFix significantly improves pseudocode readability, achieving up to a 34% reduction in Halstead Complexity Effort and a 105% increase in BLEU-4 score. Notably, it significantly outperforms state-of-the-art approaches in both temporary variable elimination and goto statement removal tasks. Additionally, human evaluations yield consistently positive feedback from users across readability, consistency, and reasonability.
This program is tentative and subject to change.
Tue 18 NovDisplayed time zone: Seoul change
11:00 - 12:30 | |||
11:00 10mTalk | Coverage-Based Harmfulness Testing for LLM Code Transformation Research Papers Honghao Tan Concordia University, Haibo Wang Concordia University, Diany Pressato Concordia University, Yisen Xu Software PErformance, Analysis, and Reliability (SPEAR) lab, Concordia University, Montreal, Canada, Shin Hwei Tan Concordia University | ||
11:10 10mTalk | RealisticCodeBench: Towards More Realistic Evaluation of Large Language Models for Code Generation Research Papers Xiao Yu Zhejiang University, Haoxuan Chen Wuhan University of Technology, Lei Liu Xi’an Jiaotong University, Xing Hu Zhejiang University, Jacky Keung City University of Hong Kong, Xin Xia Zhejiang University | ||
11:20 10mTalk | Code-DiTing: Automatic Evaluation of Code Generation without References or Test Cases Research Papers Guang Yang , Yu Zhou Nanjing University of Aeronautics and Astronautics, Xiang Chen Nantong University, Wei Zheng Northwestern Polytechnical University, Xing Hu Zhejiang University, Xin Zhou Singapore Management University, Singapore, David Lo Singapore Management University, Taolue Chen Birkbeck, University of London Pre-print | ||
11:30 10mTalk | An Agent-based Evaluation Framework for Complex Code Generation Research Papers Xinchen Wang Harbin Institute of Technology, Pengfei Gao ByteDance, Chao Peng ByteDance, Ruida Hu Harbin Institute of Technology, Shenzhen, Cuiyun Gao Harbin Institute of Technology, Shenzhen | ||
11:40 10mTalk | PseudoFix: Refactoring Distorted Structures in Decompiled C Pseudocode Research Papers Gangyang Li University of Science and Technology of China, Xiuwei Shang University of Science and Technology of China, Shaoyin Cheng University of Science and Technology of China, junqi zhang University of Science and Technology of China, Li Hu , Xu Zhu University of Science and Technology of China, Weiming Zhang University of Science and Technology of China, Nenghai Yu School of Cyber Security, University of Science and Technology of China | ||
11:50 10mTalk | Evaluating and Improving Framework-based Parallel Code Completion with Large Language Models Research Papers Ke Liu , Qinglin Wang Shandong Normal University, Xiang Chen Nantong University, Guang Yang , YiGui Feng National University of Defense Technology, Gencheng Liu National University of Defense Technology, Jie Liu Institute of Software, Chinese Academy of Sciences | ||
12:00 10mTalk | Variational Prefix Tuning for diverse and accurate code summarization using pre-trained language models Journal-First Track Junda Zhao Department of Mechanical and Industrial Engineering, University of Toronto, Yuliang Song Department of Mechanical and Industrial Engineering, University of Toronto, Eldan Cohen Department of Mechanical and Industrial Engineering, University of Toronto | ||
12:10 10mTalk | Effective Code Membership Inference for Code Completion Models via Adversarial Prompts Research Papers Yuan Jiang Harbin Institute of Technology, Zehao Li Harbin Institute of Technology, Shan Huang East China Normal University, Christoph Treude Singapore Management University, Xiaohong Su Harbin Institute of Technology, Tiantian Wang Harbin Institute of Technology | ||
12:20 10mTalk | LongCodeZip: Compress Long Context for Code Language Models Research Papers Yuling Shi Shanghai Jiao Tong University, Yichun Qian Stanford University, Hongyu Zhang Chongqing University, Beijun Shen Shanghai Jiao Tong University, Xiaodong Gu Shanghai Jiao Tong University Pre-print Media Attached | ||