• 6. Domain Adaptation for Code Model-based Unit Test Case Generation
  Jiho Shin, Sepehr Hashtroudi, Hadi Hemmati, Song Wang
  33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2024)

• 5. An Empirical Study on the Stability of Explainable Software Defect Prediction
  Jiho Shin, Reem Aleithan, Jaechang Nam, Junjie Wang, Nima Shiri Harzevili, Song Wang
  30th Asia-Pacific Software Engineering Conference (APSEC 2023)
  🏆 Distinguished Paper Award

• 4. Automatic Static Vulnerability Detection for Machine Learning Libraries: Are We There Yet?
  Nima Shiri Harzevili, Jiho Shin, Junjie Wang, Song Wang, Nachiappan Nagappan
  IEEE 34th International Symposium on Software Reliability Engineering (ISSRE 2023) 

• 3. Characterizing and Understanding Software Security Vulnerabilities in Machine Learning Libraries
  Nima Shiri Harzevili, Jiho Shin, Junjie Wang, Song Wang, Nachiappan Nagappan
  IEEE/ACM 20th International Conference on Mining Software Repositories (MSR 2023)

• 2. API Recommendation for Machine Learning Libraries: How Far Are We?
  Moshi Wei, Yuchao Huang, Junjie Wang, Jiho Shin, Nima Shiri Harzevili, Song Wang
  30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2022)

• 1. Similar Patch Recommendation for Actionable Defect Prediction
  Jiho Shin and Jaechang Nam
  Proceedings of the 22nd Korea Conference on Software Engineering (KCSE 2020)
  🏆 Distinguished Paper Award

• 4. Prompt Engineering or Fine-Tuning: An Empirical Assessment of Large Language Models in Automated Software Engineering Tasks
  Jiho Shin, Clark Tang, Tahmineh Mohati, Maleknaz Nayebi, Song Wang, Hadi Hemmati
  arXiv preprint 2023, (Under Review at TSE)

• 3. Assessing Evaluation Metrics  for Neural Test Oracle Generation
  Jiho Shin, Hadi Hemmati, Moshi Wei, Song Wang
  IEEE Transactions on Software Engineering (TSE 2024)

• 2. The Good, the Bad, and the Missing: Neural Code Generation for Machine Learning Tasks
  Jiho Shin, Moshi Wei, Junjie Wang, Lin Shi, Song Wang
  ACM Transactions on Software Engineering and Methodology (TOSEM 2023)

• 1. A Survey of Automatic Code Generation from Natural Language
  Jiho Shin and Jaechang Nam
  Journal of Information Processing Systems (JIPS 2021)

York University, Toronto 2021 ~ Present

Dept. of EECS Ph.D.

Research Field: AI/ML for Software Engineering, Code Generation, Automated Software Testing, LLM for SE

Supervisors: Dr. Song Wang and Dr. Hadi Hemmati

Handong Global University, Pohang 2019 ~ 2021

Dept. of CSEE M.Sc.

Research Field: Code Generation, Actionable and Explainable Defect Prediction

Supervisor: Dr. Jaechang Nam

Thesis: Actionable Defect Prediction

Handong Global University, Pohang 2012 ~ 2019

Dept. of CSEE B.Sc.

Specialization: Web Application, Human-Computer Interaction, Computer Vision

York University

Graduate Research Assistant Sep. 2021 ~ Present

Teaching Assistant Sep. 2021 ~ Present

Handong Global University

Graduate Research Assistant Mar. 2019 ~ Feb. 2021

Teaching Assistant Sep. 2018 ~ Dec. 2020

Republic of Korea Navy

Military Interpreter (KOR/ENG) Feb. 2014 ~ Jan. 2016

ICST'24 Organizing Committee - Web Chair for International Conference on Software Testing, Verification and Validation 2024.

Communications of Software Engineering Society- wrote a column on attending APSEC for Software Engineering Society, Sigsoft (2024/03).

TSE - Reviewer at Transactions on Software Engineering, 2024. 

CSER'24 - presented the empirical study regarding prompt engineering vs fine-tuning in ASE.

ICST'24 - organized and attended the conference as a web chair and a student volunteer.

APSEC'23 - presented XDP (eXplainable Defect Prediction) paper.

CSER'23 - presented ML-related code generation at the Consortium for Software Engineering Research.

CSER'22 - attended Consortium for Software Engineering Research.

KCSE'20 - presented the idea of change suggestions for actionable defect prediction.