Austin Mordahl
Assistant Professor
Chicago, IL
amordahl@uic.edu
he/him/his
About Me
I am an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago.
I am actively looking for students who are interested in doing cutting edge research in software engineering, so if you're a current/incoming student to UIC, or interested in moving to UIC in the future, please feel free to reach out!
Research Interests
My research focuses on transforming software quality assurance approaches to make them more accurate and accessible to a wider audience and variety of use cases. My work so has primarily focused on static analysis and fuzzing; I have developed novel methods to automatically test and debug configurable analyses, and has advanced the understanding of the full behavior of such tools. I have also worked on applying machine learning for the tasks of triaging and automatically configuring static analysis tools, lifting static analysis to work on software product lines, and on improving evaluations of fuzz testing.
Awards
NSF Graduate Research Fellowship Awardee
2020
Eugene McDermott Graduate Research Fellowship Awardee
2020
ICSE 2019 Student Research Competition Winner
2019
Publications
An Extensive Empirical Study of Nondeterministic Behavior in Static Analysis Tools
Appearing in ICSE 2025
Miao Miao, Austin Mordahl, Dakota Soles, Alice Beideck, Shiyi Wei
Automated Testing Linguistic Capabilities of NLP Models
TOSEM 2024
Jaeseong Lee, Simin Chen, Austin Mordahl, Cong Liu, Wei Yang, Shiyi Wei
RTL-Spec: RTL Spectrum Analysis for Security Bug Localization
HOST 2024
Samit Miftah, Shamik Kundu, Austin Mordahl, Shiyi Wei, and Kanad Basu
ECSTATIC: An Extensible Framework for Testing and Debugging Configurable Static Analysis
ICSE 2023
Austin Mordahl, Zenong Zhang, Dakota Soles, Shiyi Wei
ECSTATIC: Automatic Configuration-Aware Testing and Debugging of Static Analysis Tools
ISSTA 2023 Tool Demonstration
Austin Mordahl, Dakota Soles, Miao Miao, Zenong Zhang, Shiyi Wei
Automatic Testing and Benchmarking for Configurable Static Analysis Tools
ISSTA 2023 Doctoral Symposium
Austin Mordahl
An Empirical Assessment of Machine Learning Approaches for Triaging Reports of Static Analysis Tools
EMSE Vol 28, Issue 2, Article 28
Sai Yerramreddy, Austin Mordahl, Ugur Koc, Shiyi Wei, Jeffrey S. Foster, Marine Carpuat, Adam A. Porter
SATune: A Study-Driven Auto-Tuning Approach for Configurable Software Verification Tools
ASE 2021
Ugur Koc, Austin Mordahl, Shiyi Wei, Jeffrey S. Foster, and Adam Porter
The Impact of Tool Configuration Spaces on the Evaluation of Configurable Taint Analysis for Android
ISSTA 2021
Austin Mordahl, Shiyi Wei
An Empirical Study of Real-World Variability Bugs Detected by Variability-Oblivious Tools
ESEC/FSE 2019
Austin Mordahl, Jeho Oh, Ugur Koç, Shiyi Wei, Paul Gazzillo
Toward Detection and Characterization of Variability Bugs in Configurable C Software: An Empirical Study
ICSE 2019 Winner of the Student Research Competition
Austin Mordahl