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Seongmin Lee

Ph.D.

MPI-SP , Germany


CV | Google Scholar


I am a postdoctoral researcher at the Max Planck Institute for Security and Privacy in Bochum, Germany. I'm working with Dr. Marcel Böhme in the Software Security group. My research interest lies in dynamic program analysis, especially using statistical methods on dynamic information from execution to reason about a program's semantic properties. My research aims to bring program analysis closer to real-world circumstances regarding the scale and complexity of software within the presence of non-experimental or missing data in the analysis. I got my Ph.D. in Computation Intelligence and Software Engineering Lab (COINSE) at KAIST, advised by Dr. Shin Yoo in 2022.

Contact

  • seongmin.lee [at] mpi-sp.org

  • MPI-SP, Universitätsstraße 110, 44799 Bochum, Nordrhein-Westfalen, Germany

Education

  • Integrated Master's & Ph.D in School of Computing, KAIST

    - Title: Statistical Program Dependence Approximation

    Sep. 2016 - Aug. 2022

  • B.S. in School of Computing, KAIST

    (Double major: Department of Mathematical Sciences)

    Feb. 2012 - Aug. 2016

Publications

    * More publications are avilable in my Google Scholar.
    2023

  • Statistical Reachability Analysis

    Seongmin Lee, Marcel Böhme

    ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 2023

    [Paper] [Artifact]

    • 2021

  • Observation-based approximate dependency modeling and its use for program slicing

    Seongmin Lee, David Binkley, Robert Feldt, Nicolas Gold, Shin Yoo

    Journal of Systems and Software 2021

    [Paper] [Artifact] [Data statistics]

  • Causal Program Dependence Analysis

    Seongmin Lee, David Binkley, Robert Feldt, Nicolas Gold, Shin Yoo

    arXiv preprint, 2021

    [Paper]

  • Effectively sampling higher order mutants using causal effect

    Saeyoon Oh, Seongmin Lee, Shin Yoo

    IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), 2021

    [Paper]

    • 2020

  • Scalable and approximate program dependence analysis

    Seongmin Lee

    Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings, 2020

    [Paper]

  • Evaluating lexical approximation of program dependence

    Seongmin Lee, David Binkley, Nicolas Gold, Syed Islam, Jens Krinke, Shin Yoo

    Journal of Systems and Software 2020

    [Paper] [Video] [Figures]

    • 2019

  • MOAD: Modeling observation-based approximate dependency

    Seongmin Lee, David Binkley, Robert Feldt, Nicolas Gold, Shin Yoo

    19th International Working Conference on Source Code Analysis and Manipulation (SCAM), 2019

    [Paper] [Artifact] [Data statistics]

  • Classifying false positive static checker alarms in continuous integration using convolutional neural networks

    Seongmin Lee, Shin Hong, Jungbae Yi, Taeksu Kim, Chul-Joo Kim, Shin Yoo

    12th IEEE Conference on Software Testing, Validation and Verification (ICST), 2019

    [Paper]

    • 2018

  • MOBS: multi-operator observation-based slicing using lexical approximation of program dependence

    Seongmin Lee, David Binkley, Nicolas Gold, Syed Islam, Jens Krinke, Shin Yoo

    Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, 2018

    [Paper] [Figures]

    • 2017

  • PyGGI: Python General framework for Genetic Improvement

    Gabin An, Jinhan Kim, Seongmin Lee, and Shin Yoo

    Proceedings of Korea Software Congress (KSC), 2017

    [Paper]

  • Hyperheuristic Observation Based Slicing of Guava

    Seongmin Lee, and Shin Yoo

    International Symposium on Search Based Software Engineering (SSBSE), 2017

    [Paper]

    • 2016

  • Amortised deep parameter optimisation of GPGPU work group size for OpenCV

    Jeongju Sohn, Seongmin Lee, and Shin Yoo

    International Symposium on Search Based Software Engineering (SSBSE), 2016

    [Paper]

Services

Academic Services

  • Reviewer: JSS 2020, JSS 2021, TOSEM 2022
  • Program committee: ASE 2023 / (Artifact Evaluation Track) ICSME 2021, 2022, ISSTA 2023, ICSE 2024

Other Services

  • Invited speaker at 2023 Sheffield Causality and Testing Workshop on "Causal Program Dependence Analysis", 2023
  • Invited speaker at Handong Global University on "Statistical program dependence analysis", 2022
  • Invited speaker at Korea Conference on Software Engineering on "Observation-based approximate dependency modeling and its use for program slicing", 2022
  • Invited speaker at 59th CREST Open Workshop - Multi-language Software Analysis on "MOBS: Multi-Operator Observation-Based Slicing using Lexical Approximation of Program Dependence", 2018

Awards & Honors

  • PhD Dissertation Award, School of Computing, KAIST, 2022
    • - Title: Statistical Program Dependence Approximation
  • Naver Ph.D. Fellowship Award, NAVER Corp., 2021
    • - Awarded by NAVER Corp. to Ph.D. candidates who have published an outstanding research paper or have excellent publication performance.
  • Government-sponsored Scholarship, Ministry of Science and ICT of Korea, 2016 - 2022
  • Government-sponsored Scholarship, Ministry of Science and ICT of Korea, 2012 - 2016

Teaching Experiences

  • Teaching Assistant, Automated Software Testing (CS453), School of Computing, KAIST, Spring 2019
  • Teaching Assistant, Artificial Intelligence Based Software Engineering (CS454), School of Computing, KAIST, Fall 2018
  • Teaching Assistant, Introduction to Logic for Computer Science (CS402), School of Computing, KAIST, Spring 2018
  • Teaching Assistant, Artificial Intelligence Based Software Engineering (CS454), School of Computing, KAIST, Fall 2017
  • Teaching Assistant, Introduction to Logic for Computer Science (CS402), School of Computing, KAIST, Spring 2017
  • Teaching Assistant, Special Topics in Computer Science ⟨Search Based Software Engineering⟩ (CS492), School of Computing, KAIST, Fall 2016