Jinhan Kim
Title
Cited by
Cited by
Year
Guiding Deep Learning System Testing using Surprise Adequacy
J Kim, R Feldt, S Yoo
Proceedings of the 41st International Conference on Software Engineering …, 2019
1492019
Beupright: Posture correction using relational norm intervention
J Shin, B Kang, T Park, J Huh, J Kim, J Song
Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems …, 2016
222016
Comparing Line and AST Granularity Level for Program Repair using PyGGI
G An, J Kim, S Yoo
Proceedings of the 4th International Workshop on Genetic Improvement …, 2018
102018
Software review: Deap (distributed evolutionary algorithm in python) library
J Kim, S Yoo
Genetic Programming and Evolvable Machines 20 (1), 139-142, 2019
92019
Reducing dnn labelling cost using surprise adequacy: An industrial case study for autonomous driving
J Kim, J Ju, R Feldt, S Yoo
Proceedings of the 28th ACM Joint Meeting on European Software Engineering …, 2020
62020
Elicast: Embedding Interactive Exercises in Instructional Programming Screencasts
J Park, YH Park, J Kim, J Cha, S Kim, A Oh
Proceedings of the Fifth Annual ACM Conference on Learning at Scale, 58, 2018
62018
PyGGI: Python General framework for Genetic Improvement
G An, J Kim, S Lee, S Yoo
한국정보과학회 학술발표논문집, 536-538, 2017
52017
GPGPGPU: Evaluation of Parallelisation of Genetic Programming using GPGPU
J Kim, J Kim, S Yoo
International Symposium on Search Based Software Engineering, 137-142, 2017
52017
Amortising the cost of mutation based fault localisation using statistical inference
J Kim, G An, R Feldt, S Yoo
arXiv preprint arXiv:1902.09729, 2019
22019
Learning without peeking: Secure multi-party computation genetic programming
J Kim, MG Epitropakis, S Yoo
International Symposium on Search Based Software Engineering, 246-261, 2018
22018
Predictive Mutation Analysis via Natural Language Channel in Source Code
J Kim, J Jeon, S Hong, S Yoo
arXiv preprint arXiv:2104.10865, 2021
2021
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Articles 1–11