6 citations · 7 across the 11 of their papers we have counts for
40 papers
On Predicting Vulnerability Severity Using In-Context Learning: An Industrial Case Study
Daniel Rodriguez-Cardenas, David Nader Palacio, Anna Schmedding +8
Modern software systems require earlier and more scalable vulnerability severity assessment to reduce exposure to high-impact security flaws. Security analysts typically assign CVS…
Fusing UI Structure & Semantics for Feature-Oriented App Screen Retrieval & Clustering
Arun Krishna Vajjala, Yanfu Yan, Ajay Krishna Vajjala +3
User Interface (UI) programming is challenging due to the complex abstraction gap between code and graphical software representations. To bridge this gap, UI programming tools ofte…
On Automated and Explainable Provenance of AI-Generated Code
Alejandro Velasco, Nathan Wintersgill, Trevor Stalnaker +2
Generative AI for code generation has transformed software development, but it has also introduced a critical transparency problem: the origins of AI-generated code are opaque to t…
ECLAIR: A Causally-Grounded AI Framework for Scientific Discovery in Empirical Software Engineering
Alejandro Velasco, Daniel Rodriguez-Cardenas, Dipin Khati +2
The scientific method has long guided empirical research in Software Engineering (SE), but the complexity of modern software systems often hinders its systematic application. This…
Enhancing Code Understanding for Impact Analysis by Combining Transformers and Program Dependence Graphs
Yanfu Yan, Nathan Cooper, Kevin Moran +3
Impact analysis (IA) is a critical software maintenance task that identifies the effects of a given set of code changes on a larger software project with the intention of avoiding…
Rethinking Software Empirical Studies with Structural Causal Models
Daniel Rodriguez-Cardenas, Aya Garryyeva, David Nader Palacio +2
Causal Inference offers a fundamental approach for advancing empirical software engineering (ESE) beyond traditional statistical association, enabling researchers to rigorously ide…