4 citations · 14 across the 15 of their papers we have counts for
8 papers · 1 filter
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…
Enabling Global, Human-Centered Explanations for LLMs:From Tokens to Interpretable Code and Test Generation
Dipin Khati, Daniel Rodriguez-Cardenas, David N. Palacio +3
As Large Language Models for Code (LM4Code) become integral to software engineering, establishing trust in their output becomes critical. However, standard accuracy metrics obscure…
Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives
Dipin Khati, Yijin Liu, David N. Palacio +2
Applications of Large Language Models (LLMs) are rapidly growing in industry and academia for various software engineering (SE) tasks. As these models become more integral to criti…
SnipGen: A Mining Repository Framework for Evaluating LLMs for Code
Daniel Rodriguez-Cardenas, Alejandro Velasco, Denys Poshyvanyk
Language Models (LLMs), such as transformer-based neural networks trained on billions of parameters, have become increasingly prevalent in software engineering (SE). These models,…
Toward Neurosymbolic Program Comprehension
Alejandro Velasco, Aya Garryyeva, David N. Palacio +2
Recent advancements in Large Language Models (LLMs) have paved the way for Large Code Models (LCMs), enabling automation in complex software engineering tasks, such as code generat…
How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study
Alejandro Velasco, Daniel Rodriguez-Cardenas, Luftar Rahman Alif +2
Large Language Models (LLMs) have shown significant potential in automating software engineering tasks, particularly in code generation. However, current evaluation benchmarks, whi…