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20242026
most citedEvaluating LLM-Based Regression Test Generation

1 citations · 1 across the 2 of their papers we have counts for

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cs.SE20261 cited

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…

cs.SE2025

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…

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

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…

cs.SE2024

Software Security Analysis in 2030 and Beyond: A Research Roadmap

Marcel Böhme, Eric Bodden, Tevfik Bultan +3

As our lives, our businesses, and indeed our world economy become increasingly reliant on the secure operation of many interconnected software systems, the software engineering res…