45 citations · 79 across the 19 of their papers we have counts for
6 papers · 1 filter
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…
Towards a Science of Causal Interpretability in Deep Learning for Software Engineering
David N. Palacio
This dissertation addresses achieving causal interpretability in Deep Learning for Software Engineering (DL4SE). While Neural Code Models (NCMs) show strong performance in automati…
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…
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…