1 citations · 1 across the 1 of their papers we have counts for
4 papers
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…
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…
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…
How Much is Unseen Depends Chiefly on Information About the Seen
Seongmin Lee, Marcel Böhme
The missing mass refers to the proportion of data points in an unknown population of classifier inputs that belong to classes not present in the classifier's training data, which i…