3 papers
cs.SE2025
Refining Fuzzed Crashing Inputs for Better Fault Diagnosis
Kieun Kim, Seongmin Lee, Shin Hong
We present DiffMin, a technique that refines a fuzzed crashing input to gain greater similarities to given passing inputs to help developers analyze the crashing input to identify…
cs.SE2025
Cottontail: Large Language Model-Driven Concolic Execution for Highly Structured Test Input Generation
Haoxin Tu, Seongmin Lee, Yuxian Li +3
How can we perform concolic execution to generate highly structured test inputs for systematically testing parsing programs? Existing concolic execution engines are significantly r…
cs.SE2025
Evaluating LLM-Based Regression Test Generation
Jing Liu, Seongmin Lee, Eleonora Losiouk +1
Large Language Models (LLMs) have shown tremendous promise in automated software engineering. In this paper, we investigate LLMs for just-in-time regression test generation for pro…