David N. Palacio

21 papers A* 1A 3Journal 15Unranked 2
YearRankTypeTitle / Venue / Authors
2025 J jnl
A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code.
CoRR
Alejandro Velasco, Daniel Rodríguez-Cárdenas, Dipin Khati, David N. Palacio, Luftar Rahman Alif, Denys Poshyvanyk
2025 — conf
How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study.
NIER@ICSE
Alejandro Velasco, Daniel Rodríguez-Cárdenas, Luftar Rahman Alif, David N. Palacio, Denys Poshyvanyk
2025 J jnl
Mapping the Trust Terrain: LLMs in Software Engineering - Insights and Perspectives.
CoRR
Dipin Khati, Yijin Liu, David N. Palacio, Yixuan Zhang, Denys Poshyvanyk
2025 J jnl
On Explaining (Large) Language Models For Code Using Global Code-Based Explanations.
CoRR
David N. Palacio, Dipin Khati, Daniel Rodríguez-Cárdenas, Alejandro Velasco, Denys Poshyvanyk
2025 A conf
Toward Neurosymbolic Program Comprehension.
ICPC
Alejandro Velasco, Aya Garryyeva, David N. Palacio, Antonio Mastropaolo, Denys Poshyvanyk
2025 J jnl
Toward Neurosymbolic Program Comprehension.
CoRR
Alejandro Velasco, Aya Garryyeva, David N. Palacio, Antonio Mastropaolo, Denys Poshyvanyk
2025 J jnl
Towards a Science of Causal Interpretability in Deep Learning for Software Engineering.
CoRR
David N. Palacio
2024 J jnl
How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study.
CoRR
Alejandro Velasco, Daniel Rodríguez-Cárdenas, David N. Palacio, Luftar Rahman Alif, Denys Poshyvanyk
2024 J jnl
Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We?
CoRR
Conor O'Brien, Daniel Rodríguez-Cárdenas, Alejandro Velasco, David N. Palacio, Denys Poshyvanyk
2024 J jnl
On Interpreting the Effectiveness of Unsupervised Software Traceability with Information Theory.
CoRR
David N. Palacio, Daniel Rodríguez-Cárdenas, Denys Poshyvanyk, Kevin Moran
2024 J jnl
Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations.
CoRR
David N. Palacio, Daniel Rodríguez-Cárdenas, Alejandro Velasco, Dipin Khati, Kevin Moran, Denys Poshyvanyk
2024 — conf
Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code?
NIER@ICSE
Alejandro Velasco, David N. Palacio, Daniel Rodríguez-Cárdenas, Denys Poshyvanyk
2024 J jnl
Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code?
CoRR
Alejandro Velasco, David N. Palacio, Daniel Rodríguez-Cárdenas, Denys Poshyvanyk
2023 A conf
Benchmarking Causal Study to Interpret Large Language Models for Source Code.
ICSME
Daniel Rodríguez-Cárdenas, David N. Palacio, Dipin Khati, Henry Burke, Denys Poshyvanyk
2023 J jnl
Benchmarking Causal Study to Interpret Large Language Models for Source Code.
CoRR
Daniel Rodríguez-Cárdenas, David N. Palacio, Dipin Khati, Henry Burke, Denys Poshyvanyk
2023 J jnl
Evaluating and Explaining Large Language Models for Code Using Syntactic Structures.
CoRR
David N. Palacio, Alejandro Velasco, Daniel Rodríguez-Cárdenas, Kevin Moran, Denys Poshyvanyk
2023 J jnl
Toward a Theory of Causation for Interpreting Neural Code Models.
CoRR
David N. Palacio, Alejandro Velasco, Nathan Cooper, Alvaro Rodriguez, Kevin Moran, Denys Poshyvanyk
2020 J jnl
Improving the Effectiveness of Traceability Link Recovery using Hierarchical Bayesian Networks.
CoRR
Kevin Moran, David N. Palacio, Carlos Bernal-Cárdenas, Daniel McCrystal, Denys Poshyvanyk, Chris Shenefiel, Jeff Johnson
2020 A* conf
Improving the effectiveness of traceability link recovery using hierarchical bayesian networks.
ICSE
Kevin Moran, David N. Palacio, Carlos Bernal-Cárdenas, Daniel McCrystal, Denys Poshyvanyk, Chris Shenefiel, Jeff Johnson
2019 A conf
Learning to Identify Security-Related Issues Using Convolutional Neural Networks.
ICSME
David N. Palacio, Daniel McCrystal, Kevin Moran, Carlos Bernal-Cárdenas, Denys Poshyvanyk, Chris Shenefiel
2019 J jnl
Learning to Identify Security-Related Issues Using Convolutional Neural Networks.
CoRR
David N. Palacio, Daniel McCrystal, Kevin Moran, Carlos Bernal-Cárdenas, Denys Poshyvanyk, Chris Shenefiel