activity
20242026
most citedHuman-in-the-Loop Synthetic Text Data Inspection with Provenance Tracking

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.FL2026

An Empirical Comparison of General Context-Free Parsers

Huan Vo, Danushka Liyanage, Hong Jin Kang +2

Parsing underpins a vast range of software engineering tasks, from compilers and static analyzers to language servers and fuzz testing tools. Yet most parsers deployed in practice…

cs.SE2025

Targeted Testing of Compiler Optimizations via Grammar-Level Composition Styles

Zitong Zhou, Ben Limpanukorn, Hong Jin Kang +5

Ensuring the correctness of compiler optimizations is critical, but existing fuzzers struggle to test optimizations effectively. First, most fuzzers use optimization pipelines (heu…

cs.SE2025

SWIRL: Interactive Sensemaking of Tool-Generated Warnings through Customized Summaries

Burak Yetiştiren, Hong Jin Kang, Miryung Kim

Programmers using bug-finding tools often review their reported warnings one by one. Based on the insight that identifying recurring themes and relationships can enhance the cognit…

cs.HC20241 cited

Human-in-the-Loop Synthetic Text Data Inspection with Provenance Tracking

Hong Jin Kang, Fabrice Harel-Canada, Muhammad Ali Gulzar +2

Data augmentation techniques apply transformations to existing texts to generate additional data. The transformations may produce low-quality texts, where the meaning of the text i…

cs.SE2024

Fuzzing MLIR Compilers with Custom Mutation Synthesis

Ben Limpanukorn, Jiyuan Wang, Hong Jin Kang +2

Compiler technologies in deep learning and domain-specific hardware acceleration are increasingly adopting extensible compiler frameworks such as Multi-Level Intermediate Represent…