1 citations · 1 across the 1 of their papers we have counts for
5 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…
Vital: Vulnerability-Oriented Symbolic Execution via Type-Unsafe Pointer-Guided Monte Carlo Tree Search
Haoxin Tu, Lingxiao Jiang, Marcel Böhme
How to find memory safety bugs efficiently when navigating a symbolic execution tree that suffers from path explosion? Existing solutions either adopt path search heuristics to max…
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…
Fuzzing: On Benchmarking Outcome as a Function of Benchmark Properties
Dylan Wolff, Marcel Böhme, Abhik Roychoudhury
Characteristics of a benchmarking setup clearly can have some impact on the benchmark outcome. In this paper, we explore two methodologies to quantify the impact of the specific pr…
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…