Jinhan Kim

Jinhan Kim,

Postdoctoral Researcher,

Software Institute,

Università della Svizzera italiana,

Office D3.18 (Level P3), Sector D, Campus East, Via la Santa 1, 6962 Viganello, Switzerland

Email: jinhan.kim [at-symbol] usi.ch

CV: pdf

Jinhan Kim is a postdoc at TAU lab in Università della Svizzera italiana (USI) led by Prof. Paolo Tonella. He completed his Ph.D. degree from KAIST under the supervision of Prof. Shin Yoo. His research bridges software engineering and artificial intelligence, focusing on testing, reliability, and assurance of learning-enabled systems. He develops principled methods to assess and improve the robustness, interpretability, and trustworthiness of modern AI systems, from traditional softwares to LLMs, aiming to make them reliable and transparent for deployment in safety-critical domains.

News

Oct 2025:

Our paper “Revisiting “Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion”: A Critical Review and Implications on DNN Coverage Testing” has been accepted for publication in ICSE 2026.

Sep 2025:

The paper “A Taxonomy of System-Level Attacks on Deep Learning Models in Autonomous Vehicles” has been accepted for publication in TOSEM. This is the second paper of mine with my PhD student, Masoud Jamshidiyan Tehrani.

Sep 2025:

Our paper, “Evaluating and Improving the Robustness of Security Attack Detectors Generated by LLMs” has been accepted for publication in Empirical Software Engineering. This marks the first paper from my PhD student, Samuele Pasini.

Sep 2025:

I serve as a Chair of the Testing Tools and Data Showcase Track at ICST 2026.

Jul 2025:

I am organising SBFT 2026 and DeepTest 2026, both of which will be co-located with ICSE 2026.

Apr 2025:

The paper “An Empirical Study of Fault Localisation Techniques for Deep Neural Networks” has been accepted for publication in Empirical Software Engineering.

Mar 2025:

The paper “PCLA: A Framework for Testing Autonomous Agents in the CARLA Simulator” has been accepted for FSE 2025 (Demonstrations Track).

Feb 2025:

I joined the program committee of ASE 2025 (NIER Track, Student Research Competition Track) and DeMeSSAI 2025.

Sep 2024:

I joined the program committee of SBFT 2025 and Mutation 2025.

Aug 2024:

I am organising DeepTest 2025, co-located with ICSE 2025.

Apr 2024:

I joined the program committee of ASE 2024 (Demonstrations Track, NIER Track).

Apr 2024:

I joined the program committee of ISSTA 2025 (Research Track).

Apr 2024:
Sep 2023:

I’m thrilled to share that I’m beginning a postdoc at USI Lugano under the guidance of Prof. Paolo Tonella!

Apr 2023:

Our paper wins a best paper award at Mutation 2023.

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.

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Publications

For an up-to-date list of publications and their citation counts, please see my profile on Google Scholar.
Unless otherwise specified, they have been published at the main research track.

Journals

  1. [TOSEM] A Taxonomy of System-Level Attacks on Deep Learning Models in Autonomous Vehicles

    M. Tehrani, J. Kim, R. Foulefack, A. Marchetto and P. Tonella

    ACM Transactions on Software Engineering and Methodology, 2025

  2. [EMSE] Evaluating and Improving the Robustness of Security Attack Detectors Generated by LLMs

    S. Pasini, J. Kim, T. Aiello, R. Lozoya, A. Sabetta and P. Tonella

    Empirical Software Engineering, 2025

  3. [EMSE] An Empirical Study of Fault Localisation Techniques for Deep Neural Networks

    N. Humbatova, J. Kim, G. Jahangirova, S. Yoo and P. Tonella

    Empirical Software Engineering, 2025

  4. [IST] Learning Test-Mutant Relationship for Accurate Fault Localisation

    J. Kim, G. An, R. Feldt and S. Yoo

    Information and Software Technology, 2023

  5. [TOSEM] Evaluating Surprise Adequacy for Deep Learning System Testing

    J. Kim, R. Feldt and S. Yoo

    ACM Transactions on Software Engineering and Methodology, 2023

  6. [TOSEM] Predictive Mutation Analysis via Natural Language Channel in Source Code

    J. Kim, J. Jeon, S. Hong and S. Yoo

    ACM Transactions on Software Engineering and Methodology, 2022

    Presented at the journal-first track of ICSE 2023
Conferences & Workshops (Full Papers)

  1. [ICSE] Revisiting "Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion": A Critical Review and Implications on DNN Coverage Testing

    J. Kim, N. Humbatova, G. Jahangirova, S. Yoo and P. Tonella

    Proceedings of the 48th International Conference on Software Engineering, 2026

  2. [Mutation] The Inversive Relationship Between Bugs and Patches: An Empirical Study

    J. Kim, J. Park and S. Yoo

    Proceedings of the 18th International Workshop on Mutation Analysis, 2023

    Best Paper Award

  3. [ICST] Repairing DNN Architecture: Are We There Yet?

    J. Kim, N. Humbatova, G. Jahangirova, P. Tonella and S. Yoo

    Proceedings of the 16th IEEE International Conference on Software Testing, Verification and Validation, 2023

  4. [ICST Industry] Repairing Fragile GUI Test Cases Using Word and Layout Embedding

    J. Yoon, S. Chung, K. Shin, J. Kim, S. Hong and S. Yoo

    Proceedings of the 15th IEEE International Conference on Software Testing, Verification and Validation, 2022

  5. [ISSRE] Ahead of Time Mutation Based Fault Localisation Using Statistical Inference

    J. Kim, G. An, R. Feldt and S. Yoo

    Proceedings of the 32nd International Symposium on Software Reliability Engineering, 2021

    Invited for a special issue of IST Journal

  6. [FSE Industry] Reducing DNN Labelling Cost Using Surprise Adequacy: An Industrial Case Study for Autonomous Driving

    J. Kim, J. Ju, R. Feldt and S. Yoo

    Proceedings of ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2020

  7. [ICSE] Guiding Deep Learning System Testing Using Surprise Adequacy

    J. Kim, R. Feldt and S. Yoo

    Proceedings of the 41st International Conference on Software Engineering, 2019

  8. [L@S] Elicast: Embedding Interactive Exercises in Instructional Programming Screencasts

    J. Park, Y. Park, J. Kim, J. Cha, S. Kim and A. Oh

    Proceedings of the Fifth Annual ACM Conference on Learning at Scale, 2018

  9. [SSBSE] Learning Without Peeking: Secure Multi-Party Computation Genetic Programming

    J. Kim, M. Epitropakis and S. Yoo

    Proceedings of the 10th International Symposium on Search Based Software Engineering, 2018

  10. [GI] Comparing Line and AST Granularity Level for Program Repair Using PyGGI

    G. An, J. Kim and S. Yoo

    Proceedings of the 4th Genetic Improvement Workshop, 2018

  11. [CHI] BeUpright: Posture Correction Using Relational Norm Intervention

    J. Shin, B. Kang, T. Park, J. Huh, J. Kim and J. Song

    Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 2016

Short, Poster, Demo, Domestic Papers

  1. [FSE Demo] PCLA: A Framework for Testing Autonomous Agents in the CARLA Simulator

    M. Tehrani, J. Kim and P. Tonella

    Proceedings of ACM on the Foundations of Software Engineering, 2025

  2. [KCSE] PyGGI: Python General framework for Genetic Improvement

    G. An, J. Kim, S. Lee and S. Yoo

    Proceedings of Korea Software Congress, 2017

  3. [SSBSE Short Papers] GPGPGPU: Evaluation of Parallelisation of Genetic Programming Using GPGPU

    J. Kim, J. Kim and S. Yoo

    Proceedings of the 9th International Symposium on Search Based Software Engineering, 2017

  4. [SenSys Demo] Demo: Posture Correction Using Smartphone-Based Relational Intervention Model

    J. Shin, B. Kang, J. Kim, J. Huh, J. Song and T. Park

    Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, 2015

Theses

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

    J. Kim

    Ph.D. Thesis, KAIST, February 2023

Community Services

Conference Organising Committee
Conference Program Committee
Journal Board
Journal Reviewer