Faculty Research Profile
Mijung Kim
학력
• Ph.D Hong Kong University of Science and Technology, Hong Kong, China
• M.S. Georgia Institute of Technology, Georgia, USA
• B.S. University of Illinois at Urbana-Champaign, Illinois, USA
주요 경력
• 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
소프트웨어 테스팅 및 분석 연구실
Software Testing and Analysis Research Lab
저희 소프트웨어 테스팅 및 분석 연구실에서는 소프트웨어의 신뢰성, 보안, 안정성을 향상시키기 위한 자동화 테스팅 기술을 개발하고 있습니다. 우리 기술은 테스트 자동 생성, 퍼징, 회귀 테스트 등 다양한 방식으로 테스팅을 수행합니다. 또한, 본 연구실에서는 기술 개발뿐만 아니라 개발된 기술을 오픈소스 소프트웨어 및 인공지능 시스템 등 실제 시스템에 적용하고 검증하는 것을 목표로 합니다.
In STAR(Software Testing and Analysis Research) lab, we are developing automated techniques for improving software reliability, securities, and trustworthiness. Our techniques perform test generation, fuzzing, regression testing. We also validate such techniques on real-world software systems, such as open-source projects and Artificial Intelligence(AI) software systems.
연구분야
Test Generation, Fuzzing, Regression Testing, Search-based Software Testing
Test Generation, Fuzzing, Regression Testing, Search-based Software Testing
연구주제
Automated Test Generation for improving bug detection: Test Generation for Diverse Program Behavior, Fuzzing for security vulnerability detection
Automated Test Generation for improving bug detection: Test Generation for Diverse Program Behavior, Fuzzing for security vulnerability detection
논문
• Sehoon Kim, Yonghyeon Kim, Dahyeon Park, Yuseok Jeon, Jooyong Yi, Mijung Kim. Lightweight Concolic Testing via Path-Condition Synthesis for Deep Learning Libraries. ICSE 2025.
• Danning Xie*, Byoungwoo Yoo*, Nan Jiang, Mijung Kim, Lin Tan, Xiangyu Zhang, Judy Lee. How Effective are Large Language Models in Generating Software Specifications? SANER 2025. ToAppear. *: co-first authors.
• Testing Diverse Geographical Features of Autonomous Driving Systems Seongdeok Seo, Judy Lee, Mijung Kim. Testing Diverse Geographical Features of Autonomous Driving Systems. ISSRE 2024. 439-450.
• Danning Xie, Yitong Li, Mijung Kim, Huan Viet Pham, Lin Tan, Xiangyu Zhang, Mike Godfrey. DocTer: Documentation-Guided Fuzzing for Testing Deep Learning API Functions. ISSTA 2002. 176-188.
• Fuxiang Chen, Mijung Kim, jaegul Choo. "Novel Natural Language Summarization of Program Code via Leveraging Multiple Input Representations" EMNLP 2021.
특허
• Kunal Taneja, Teresa Tung, and Mijung Kim. Testing Framework for Policy-based Workflows. US Patent #20150095895. April, 2015.
• Kunal Taneja, Teresa Tung, and Mijung Kim. Testing Framework for Policy-based Workflows. European Patent #14187280.4-1951. October, 2014.