Scholar
Mijung Kim
Google Scholar ID: g4ASrlcAAAAJ
UNIST, South Korea
Citations & Impact
Citations
258
H-index
7
i10-index
6
Publications
18
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- CMASan: Custom Memory Allocator-aware Address Sanitizer, S&P 2025 (To Appear)
- Lightweight Concolic Testing via Path-Condition Synthesis for Deep Learning Libraries, ICSE 2025 (To Appear)
- How Effective are Large Language Models in Generating Software Specifications?, SANER 2025 (To Appear)
- Testing Diverse Geographical Features of Autonomous Driving Systems, ISSRE 2024, 439-450
- DocTer: Documentation-Guided Fuzzing for Testing Deep Learning API Functions, ISSTA 2022, 176-188
- DEVIATE: A Deep Learning Variance Testing Framework, ASE 2021 Tool
- Novel Natural Language Summarization of Program Code via Leveraging Multiple Input Representations, EMNLP 2021
Research Experience
- Associate Professor at UNIST, South Korea
- Assistant Professor at UNIST, South Korea
- Postdoc at Purdue University, USA
- Visiting scholar at Southern University of Science and Technology, Shenzhen, China
- Research assistant at Hong Kong University of Science and Technology, Hong Kong, China
- Research intern at Accenture Technology Lab, San Jose, California, USA
- Graduate research assistant at Georgia Institute of Technology, USA
- Software engineer at Samsung Electronics Mobile Division, South Korea
Background
- Mijung is an associate professor at the Computer Science and Engineering department and AI Graduate School of UNIST (Ulsan National Institute of Science and Technology), South Korea. She leads the Software Testing and Analysis Research Lab at UNIST. Her research focuses on automated software testing with an emphasis on test generation and its practical usage, particularly in the artificial intelligence domain. Her interests also include fuzzing, search-based software engineering, regression testing, and defect prediction.
Co-authors