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Yi Jou Li

dblp:433/2438 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2026
0009-0002-8293-348XORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Systems and software security · 100%
Software engineering, system software, and programming languages
1 paper
Program analysis · 100%
Human-computer interaction and pervasive computing
1 paper
Usability and user experience research · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Systems and software security › vulnerability discovery
symbolic execution
1.012026
I Can SE Clearly Now: Investigating the Effectiveness of GUI-based Symbolic Execution for Software Vulnerability Discovery · CHI 2026
Systems and software security
vulnerability discovery
1.012026
I Can SE Clearly Now: Investigating the Effectiveness of GUI-based Symbolic Execution for Software Vulnerability Discovery · CHI 2026
Program analysis
dynamic analysis
1.012026
I Can SE Clearly Now: Investigating the Effectiveness of GUI-based Symbolic Execution for Software Vulnerability Discovery · CHI 2026
Program analysis
symbolic execution
1.012026
I Can SE Clearly Now: Investigating the Effectiveness of GUI-based Symbolic Execution for Software Vulnerability Discovery · CHI 2026
Usability and user experience research
tool usability
0.312026
I Can SE Clearly Now: Investigating the Effectiveness of GUI-based Symbolic Execution for Software Vulnerability Discovery · CHI 2026

Methods — techniques the papers use, named apart from their topics

controlled experiment · 3.0
YearPublicationVenuePosition
2026 I Can SE Clearly Now: Investigating the Effectiveness of GUI-based Symbolic Execution for Software Vulnerability Discovery
abstract
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.
Yi Jou Li, Zeming Yu, James Mattei, Ananta Soneji, Ruoyu Wang 0001, Jaron Mink, Daniel Votipka, Tiffany Bao
CHI1