Learning a Strategy for Choosing Widening Thresholds from a Large Codebase. - Sooyoung Cha; Sehun Jeong; Hakjoo Oh (APLAS, 2016)

Automatically generating search heuristics for concolic testing. - Sooyoung Cha; Seongjoon Hong; Junhee Lee; Hakjoo Oh (ICSE, 2018)

Template-guided concolic testing via online learning. - Sooyoung Cha; Seonho Lee; Hakjoo Oh (ASE, 2018)

A scalable learning algorithm for data-driven program analysis. - Sooyoung Cha; Sehun Jeong; Hakjoo Oh (Inf. Softw. Technol., 2018)

Concolic testing with adaptively changing search heuristics. - Sooyoung Cha; Hakjoo Oh (ESEC/SIGSOFT FSE, 2019)

Enhancing Dynamic Symbolic Execution by Automatically Learning Search Heuristics. - Sooyoung Cha; Seongjoon Hong; Jingyoung Kim; Junhee Lee; Hakjoo Oh (CoRR, 2019)

Effective white-box testing of deep neural networks with adaptive neuron-selection strategy. - Seokhyun Lee; Sooyoung Cha; Dain Lee; Hakjoo Oh (ISSTA, 2020)

Making symbolic execution promising by learning aggressive state-pruning strategy. - Sooyoung Cha; Hakjoo Oh (ESEC/SIGSOFT FSE, 2020)

SYMTUNER: Maximizing the Power of Symbolic Execution by Adaptively Tuning External Parameters. - Sooyoung Cha; Myungho Lee; Seokhyun Lee; Hakjoo Oh (ICSE, 2022)

Enhancing Dynamic Symbolic Execution by Automatically Learning Search Heuristics. - Sooyoung Cha; Seongjoon Hong; Jiseong Bak; Jingyoung Kim; Junhee Lee; Hakjoo Oh (IEEE Trans. Software Eng., 2022)

Learning Seed-Adaptive Mutation Strategies for Greybox Fuzzing. - Myungho Lee; Sooyoung Cha; Hakjoo Oh (ICSE, 2023)

FeatMaker: Automated Feature Engineering for Search Strategy of Symbolic Execution. - Jaehan Yoon; Sooyoung Cha (Proc. ACM Softw. Eng., 2024)

Topseed: Learning Seed Selection Strategies for Symbolic Execution from Scratch. - Jaehyeok Lee; Sooyoung Cha (ICSE, 2025)