Jeongju Sohn

PhD Candidate
School of Computing
Korea Advanced Institute of Science and Technology
291 Daehak Ro, Yuseong Gu
Daejeon 34141
Republic of Korea

Research Interests

Software engineering, Search-based software engineering, Software testing, Continuous Integration, Computational intelligence, Program analysis.

BSc., Computer Science and Engineering, Ewha Womans University, Republic of Korea, Mar. 2011 - Feb. 2015
MSc., Computer Science, Korean Advanced Institute of Science and Technology, Republic of Korea, Mar. 2015 - Feb. 2017

Database and Multimedia Lab, KAIST, Mar. 2015 - Jan. 2016
- Research on parallel processing architecture that intergrates the relational DBMS with the DFS as a single system for big data analysis
Computational Intelligence for Software Engineering Labroatory(COINSE), KAIST, Feb. 2016 - Present
- Research on parameter optimization on ‘OpenCV’, using amortized deep parameter optimization
- Research on finding possible links between fault localization and defect prediction to improve the performance of fault localization

Sohn, J. & Yoo, S. (2019). Empirical Evaluation of Fault Localisation Using Code and Change Metrics. IEEE Transactions on Software Engineering
Sohn, J. & Yoo, S. (2019). Why Train-and-Select When You Can Use Them All: Ensemble Model for Fault Localisation. In Proceedings of the Genetic and Evolutionary Computation Conference. Prague, Czech
Choi, K, Sohn, J., & Yoo, S. (2018). Learning Fault Localisation for Both Humans and Machines using Multi-Objective GP. In Proceeding of the Search Based Software Engineering. Montpellier, France
Kang, D., Sohn, J., & Yoo, S. (2017). Empirical evaluation of conditional operators in GP based fault localization. In Proceedings of the Genetic and Evolutionary Computation Conference. Berlin, Germany
Sohn, J. & Yoo, S. (2017). Using Code and Change Metrics to Improve Fault Localization. In Proceeding of the 2017 International Symposium on Softare Testing and Analysis. Santa Barbara, CA, USA
Sohn, J., Lee, S., & Yoo, S. (2016). Amortised Deep Parameter Optimisation of GPGPU Work Group Size for OpenCV. In Proceeding of the 2016 Search Based Software Engineering (pp. 211–217). Raleigh, NC, USA: ACM. doi:10.1007/978-3-319-47106-8_14

‘FLUCCs’: Using source code metrics, have been widely used in defect prediction, to improve Fault Localization; investigating possible relations between defect prediction and fault localization and applying revealed relations to fault localization for further improvement
‘DPFL’: Combining defect prediction and fault localisation