Enhancing Software Binary Understanding with Static Analysis

PhD Qualifying Examination


Title: "Enhancing Software Binary Understanding with Static Analysis"

by

Mr. Chengfeng YE


Abstract:

Software binaries are the final products of the software development process,
serving as the foundation for various applications such as bug detection,
security analysis, malware analysis, supply chain security, and binary
similarity analysis. However, analyzing binaries presents unique challenges
compared to source code analysis, as binaries operate at a low level with all
high-level information, such as types and symbols, often stripped away during
compilation and distribution. This loss of information creates two
fundamental challenges: (1) interpreting low-level operations to understand
program semantics, and (2) recovering the lost high-level information to
facilitate human understanding.

This survey systematically reviews static binary analysis techniques from two
complementary perspectives. On the one hand, we examine techniques for
interpreting low-level operations and discuss how these techniques have
evolved to address unique binary-level challenges through improvements in
memory modeling and in address representation. On the other hand, we
investigate techniques for recovering high-level information from stripped
binaries, organized from lower to higher levels of abstraction. We cover Data
Layout Analysis for recovering variables and composite structures from memory
access patterns, Type Inference techniques for inferring high-level type
information, Name Inference methods to predict symbol names, and Class
Hierarchy Analysis for reconstructing object-oriented program structures in
C++ binaries.


Date:                   Tuesday, 27 January 2026

Time:                   1:00pm - 3:00pm

Venue:                  Room 2610
                        Lift 31/32

Committee Members:      Prof. Charles Zhang (Supervisor)
                        Dr. Dimitris Papadopoulos (Chairperson)
                        Dr. Dongdong She