Wed 6 Dec 2023 12:00 - 12:15 at Golden Gate C3 - Program Analysis II Chair(s): Nico Rosner

To avoid the exposure of original source code, the variable names deployed in the wild are often replaced by short, meaningless names, thus making the code difficult to understand and be analyzed. We introduce DeMinify, a Deep-Learning (DL)-based approach that formulates such recovery problem as predicting the missing features in the Graph Convolutional Network–Missing Features. The graph represents both the relations among the variables and those among their types, in which names/types of some nodes are missing. Moreover, DeMinify leverages dual-task learning to propagate the mutual impact between the learning of the variable names and that of their types. We conducted experiments to evaluate DeMinify in both name recovery and type prediction on a real-world dataset with 180k Python methods. For variable name prediction, in 76.7% of the cases, DeMinify can correctly predict the variables’ names with a single suggested name. DeMinify relatively improves from 15.3–40.7% in top-1 accuracy over the state-of-the-art variable name recovery approaches. It relatively improves 14.5%–51.9% in top-1 accuracy over the existing type prediction approaches. We showed that learning of types help improve variable name recovery.

Wed 6 Dec

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

11:00 - 12:30
Program Analysis IIResearch Papers / Journal First at Golden Gate C3
Chair(s): Nico Rosner Amazon Web Services
11:00
15m
Talk
[Remote] OOM-Guard: Towards Improving The Ergonomics of Rust OOM Handling via A Reservation-based Approach
Research Papers
Chengjun Chen Fudan University; Ant Group, Zhicong Zhang Fudan University, Hongliang Tian Ant Group, Shoumeng Yan Ant Group, Hui Xu Fudan University
Media Attached
11:15
15m
Talk
[Remote] DeepInfer: Deep Type Inference from Smart Contract Bytecode
Research Papers
Kunsong Zhao The Hong Kong Polytechnic University, Zihao Li The Hong Kong Polytechnic Universituy, Jianfeng Li Xi’an Jiaotong University, He Ye KTH Royal Institute of Technology, Xiapu Luo Hong Kong Polytechnic University, Ting Chen University of Electronic Science and Technology of China
Media Attached
11:30
15m
Talk
Statistical Type Inference for Incomplete Programs
Research Papers
Yaohui Peng School of Computer Science, Wuhan University, Jing Xie School of Computer Science, Wuhan University, Qiongling Yang School of Computer Science, Wuhan University, Hanwen Guo School of Computer Science, Wuhan University, Qingan Li School of Computer Science, Wuhan University, Jingling Xue School of Computer Science and Engineering, UNSW Sydney, YUAN Mengting School of Computer Science, Wuhan University, Wuhan, China
Media Attached
11:45
15m
Talk
Demystifying Hidden Sensitive Operations in Android apps
Journal First
Xiaoyu Sun Australian National University, Australia, Xiao Chen Monash University, Li Li Beihang University, Haipeng Cai Washington State University, John Grundy Monash University, Jordan Samhi University of Luxembourg, Tegawendé F. Bissyandé University of Luxembourg, Jacques Klein University of Luxembourg
Media Attached
12:00
15m
Talk
DeMinify: Neural Variable Name Recovery and Type Inference
Research Papers
Yi Li New Jersey Institute of Technology, Aashish Yadavally The University of Texas at Dallas, Jiaxing Zhang New Jersey Institute of Technology, Shaohua Wang Central University of Finance and Economics , Tien N. Nguyen University of Texas at Dallas
Media Attached