4 citations · 15 across the 20 of their papers we have counts for
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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…
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,…
Testing Practices, Challenges, and Developer Perspectives in Open-Source IoT Platforms
Daniel Rodriguez-Cardenas, Safwat Ali Khan, Prianka Mandal +3
As the popularity of Internet of Things (IoT) platforms grows, users gain unprecedented control over their homes, health monitoring, and daily task automation. However, the testing…
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…