4 citations · 11 across the 14 of their papers we have counts for
14 papers · 1 filter
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…
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…
How Do Semantically Equivalent Code Transformations Impact Membership Inference on LLMs for Code?
Hua Yang, Alejandro Velasco, Thanh Le-Cong +3
The success of large language models for code relies on vast amounts of code data, including public open-source repositories, such as GitHub, and private, confidential code from co…
Understanding Privacy Risks in Code Models Through Training Dynamics: A Causal Approach
Hua Yang, Alejandro Velasco, Sen Fang +2
Large language models for code (LLM4Code) have greatly improved developer productivity but also raise privacy concerns due to their reliance on open-source repositories containing…
A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code
Alejandro Velasco, Daniel Rodriguez-Cardenas, Dipin Khati +3
Recent advances in large language models (LLMs) have accelerated their adoption in software engineering contexts. However, concerns persist about the structural quality of the code…
On the Effectiveness of LLM-as-a-judge for Code Generation and Summarization
Giuseppe Crupi, Rosalia Tufano, Alejandro Velasco +3
Large Language Models have been recently exploited as judges for complex natural language processing tasks, such as Q&A. The basic idea is to delegate to an LLM the assessment of t…