I Can SE Clearly Now: Investigating the Effectiveness of GUI-based Symbolic Execution for Software Vulnerability Discovery

要旨

While symbolic execution (SE) can discover software vulnerabilities, it has received limited practical adoption. A key barrier is that SE requires human expertise to understand the program’s state and prioritize paths to analyze. Traditionally, users controlled SE through programmatic API calls, but recent tooling now implements graphical user interfaces (GUI). However, it is unclear how these new features affect human-SE performance. To understand this impact, we conducted a controlled experiment where 24 vulnerability discovery experts were tasked with analyzing a binary using an SE tool with either API or GUI-based features. From this study, we identify (1) experts' SE process, and (2) the impact of GUI-based features on human-SE performance. Then we propose recommendations to improve SE tool design.

著者
Yi Jou Li
Arizona State University, Tempe, Arizona, United States
Zeming Yu
Arizona State University, Tempe, Arizona, United States
James A. Mattei
Tufts University, Medford, Massachusetts, United States
Ananta Soneji
Arizona State University, Tempe, Arizona, United States
Zhibo Sun
Drexel University, Philladelphia, Pennsylvania, United States
Ruoyu “Fish” Wang
Arizona State University, Tempe, Arizona, United States
Jaron Mink
Arizona State University, Tempe, Arizona, United States
Daniel Votipka
Tufts University, Medford, Massachusetts, United States
Tiffany Bao
Arizona State University, Tempe, Arizona, United States

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Privacy and Security in Software Development

Area 1 + 2 + 3: theatre
7 件の発表
2026-04-16 18:00:00
2026-04-16 19:30:00