19 citations · 21 across the 5 of their papers we have counts for
6 papers
Correct Verdicts, Flawed Reasoning: Structured Auditing of LLM-based Vulnerability Reasoning
Boyue Caroline Hu, Kaivalya Ahir, Ronghao Ni +1
Large Language Models (LLMs) are increasingly deployed for automated software vulnerability analysis. Binary classification alone is insufficient; practitioners need explanations t…
Debugging and Runtime Analysis of Neural Networks with VLMs (A Case Study)
Boyue Caroline Hu, Divya Gopinath, Corina S. Pasareanu +3
Debugging of Deep Neural Networks (DNNs), particularly vision models, is very challenging due to the complex and opaque decision-making processes in these networks. In this paper,…
A Software Engineering Perspective on Testing Large Language Models: Research, Practice, Tools and Benchmarks
Sinclair Hudson, Sophia Jit, Boyue Caroline Hu +1
Large Language Models (LLMs) are rapidly becoming ubiquitous both as stand-alone tools and as components of current and future software systems. To enable usage of LLMs in the high…
Concept-based Analysis of Neural Networks via Vision-Language Models
Ravi Mangal, Nina Narodytska, Divya Gopinath +4
The analysis of vision-based deep neural networks (DNNs) is highly desirable but it is very challenging due to the difficulty of expressing formal specifications for vision tasks a…
Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance
Huakun Shen, Boyue Caroline Hu, Krzysztof Czarnecki +2
While Neural Networks (NNs) have surpassed human accuracy in image classification on ImageNet, they often lack robustness against image corruption, i.e., corruption robustness. Yet…
If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components
Boyue Caroline Hu, Lina Marsso, Krzysztof Czarnecki +3
Machine Vision Components (MVC) are becoming safety-critical. Assuring their quality, including safety, is essential for their successful deployment. Assurance relies on the availa…