Jinhan Kim

Jinhan Kim,

Ph.D Student,

COINSE Lab,

School of Computing,

KAIST,

291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea

email: jinhankim [at-symbol] kaist.ac.kr

Curriculum Vitae

Jinhan Kim is a fifth-year Ph.D student at COINSE Lab in KAIST. He generally works in the area of software engineering, currently focusing on mutation testing, fault localisation, and deep learning system testing. He received his B.S from School of Computing at KAIST, and started integrated M.S and Ph.D program since 2017 under the supervision of Dr. Shin Yoo.

News

Dec 2022:
In collaboration with TAU Lab, our paper "Repairing DNN Architecture: Are We There Yet?" has been accepted to publication at ICST 2023 (Research Papers Track).
Dec 2022:
I'm delighted to announce that I have successfully defended my Ph.D. thesis!
Aug 2022:
I'll visit Prof. Paolo Tonella at Università della Svizzera italiana (USI) for two months.
Jul 2022:
I successfully completed my dissertation proposal.

Publications

See the list at Google Scholar

Journals

  1. Evaluating Surprise Adequacy for Deep Learning System Testing

    Jinhan Kim, Robert Feldt, Shin Yoo

    TOSEM 2023 | pdf

  2. Predictive Mutation Analysis via Natural Language Channel in Source Code

    Jinhan Kim, Juyoung Jeon, Shin Hong, Shin Yoo

    TOSEM 2022 | pdf

Conferences & Workshops (Full Papers)

  1. Repairing DNN Architecture: Are We There Yet?

    Jinhan Kim, Nargiz Humbatova, Gunel Jahangirova, Paolo Tonella, Shin Yoo

    ICST 2023 | pdf

  2. Repairing Fragile GUI Test Cases Using Word and Layout Embedding

    Juyeon Yoon, Seungjoon Chung, Kihyuck Shin, Jinhan Kim, Shin Hong, Shin Yoo

    ICST 2022 Industry Track | pdf

  3. Ahead of Time Mutation Based Fault Localisation Using Statistical Inference

    Jinhan Kim, Gabin An, Robert Feldt, Shin Yoo

    ISSRE 2021 | pdf | Invited for a special issue of IST Journal

  4. Reducing DNN Labelling Cost Using Surprise Adequacy: An Industrial Case Study for Autonomous Driving

    Jinhan Kim, Jeongil Ju, Robert Feldt, Shin Yoo

    ESEC/FSE 2020 Industry Track | pdf

  5. Guiding Deep Learning System Testing Using Surprise Adequacy

    Jinhan Kim, Robert Feldt, Shin Yoo

    ICSE 2019 | pdf

  6. Elicast: Embedding Interactive Exercises in Instructional Programming Screencasts

    Jungkook Park, Yeong Hoon Park, Jinhan Kim, Jeongmin Cha, Suin Kim, Alice Oh

    L@S 2018

  7. Learning Without Peeking: Secure Multi-Party Computation Genetic Programming

    Jinhan Kim, Michael G. Epitropakis, Shin Yoo

    SSBSE 2018

  8. Comparing Line and AST Granularity Level for Program Repair Using PyGGI

    Gabin An, Jinhan Kim, Shin Yoo

    GI@ICSE 2018

  9. BeUpright: Posture Correction Using Relational Norm Intervention

    Jaemyung Shin, Bumsoo Kang, Taiwoo Park, Jina Huh, Jinhan Kim, Junehwa Song

    CHI 2016

Short, Poster, Demo, Domestic Papers

  1. PyGGI: Python General framework for Genetic Improvement

    Gabin An, Jinhan Kim, Seongmin Lee, Shin Yoo

    KCSE 2017

  2. GPGPGPU: Evaluation of Parallelisation of Genetic Programming Using GPGPU

    Jinhan Kim, Junhwi Kim, Shin Yoo

    SSBSE 2017 Short Papers Track

  3. Demo: Posture Correction Using Smartphone-Based Relational Intervention Model

    Jaemyung Shin, Bumsoo Kang, Jinhan Kim, Jina Huh, Junehwa Song, Taiwoo Park

    SenSys 2015

Theses

  1. Exploiting Mutant’s Relationship with Code, Faults, and Patches for Higher Efficacy of Mutation Analysis

    Jinhan Kim

    Ph.D. Thesis, KAIST, February 2023

In Submission

  1. Learning Test-Mutant Relationship for Accurate Fault Localisation

    Jinhan Kim, Gabin An, Robert Feldt, Shin Yoo

    Submitted in 2022 as a journal paper (under major revision)

Academic Services

Program Committee
Reviewer