Assessing single-objective performance convergence and time complexity for refactoring detection. - David Nader-Palacio; Daniel Rodríguez-Cárdenas; Jonatan Gómez (GECCO (Companion), 2018)

Learning to Identify Security-Related Issues Using Convolutional Neural Networks. - David N. Palacio; Daniel McCrystal; Kevin Moran; Carlos Bernal-Cárdenas; Denys Poshyvanyk; Chris Shenefiel (ICSME, 2019)

Improving the effectiveness of traceability link recovery using hierarchical bayesian networks. - Kevin Moran; David N. Palacio; Carlos Bernal-Cárdenas; Daniel McCrystal; Denys Poshyvanyk; Chris Shenefiel; Jeff Johnson (ICSE, 2020)

Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks. - Antonio Mastropaolo; Simone Scalabrino; Nathan Cooper; David Nader-Palacio; Denys Poshyvanyk; Rocco Oliveto; Gabriele Bavota (ICSE, 2021)

A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research. - Cody Watson; Nathan Cooper; David Nader-Palacio; Kevin Moran; Denys Poshyvanyk (ACM Trans. Softw. Eng. Methodol., 2022)

Replication Package for: Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks. - Antonio Mastropaolo; Simone Scalabrino; Nathan Cooper; David Nader-Palacio; Denys Poshyvanyk; Rocco Oliveto; Gabriele Bavota (Zenodo, 2022)

Benchmarking Causal Study to Interpret Large Language Models for Source Code. - Daniel Rodríguez-Cárdenas; David N. Palacio; Dipin Khati; Henry Burke; Denys Poshyvanyk (ICSME, 2023)

Evaluating and Explaining Large Language Models for Code Using Syntactic Structures. - David N. Palacio; Alejandro Velasco; Daniel Rodríguez-Cárdenas; Kevin Moran; Denys Poshyvanyk (CoRR, 2023)

Using Transfer Learning for Code-Related Tasks. - Antonio Mastropaolo; Nathan Cooper; David Nader-Palacio; Simone Scalabrino; Denys Poshyvanyk; Rocco Oliveto; Gabriele Bavota (IEEE Trans. Software Eng., 2023)

Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code? - Alejandro Velasco; David N. Palacio; Daniel Rodríguez-Cárdenas; Denys Poshyvanyk (NIER@ICSE, 2024)

Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations. - David N. Palacio; Daniel Rodríguez-Cárdenas; Alejandro Velasco; Dipin Khati; Kevin Moran; Denys Poshyvanyk (CoRR, 2024)

Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We? - Conor O'Brien; Daniel Rodríguez-Cárdenas; Alejandro Velasco; David N. Palacio; Denys Poshyvanyk (CoRR, 2024)

Perspective of Software Engineering Researchers on Machine Learning Practices Regarding Research, Review, and Education. - Anamaria Mojica-Hanke; David Nader-Palacio; Denys Poshyvanyk; Mario Linares-Vásquez; Steffen Herbold (CoRR, 2024)

On Interpreting the Effectiveness of Unsupervised Software Traceability with Information Theory. - David N. Palacio; Daniel Rodríguez-Cárdenas; Denys Poshyvanyk; Kevin Moran (CoRR, 2024)

Toward a Theory of Causation for Interpreting Neural Code Models. - David Nader-Palacio; Alejandro Velasco; Nathan Cooper; Alvaro Rodriguez; Kevin Moran; Denys Poshyvanyk (IEEE Trans. Software Eng., 2024)

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study. - Alejandro Velasco; Daniel Rodríguez-Cárdenas; Luftar Rahman Alif; David N. Palacio; Denys Poshyvanyk (NIER@ICSE, 2025)

Toward Neurosymbolic Program Comprehension. - Alejandro Velasco; Aya Garryyeva; David N. Palacio; Antonio Mastropaolo; Denys Poshyvanyk (ICPC, 2025)

Mapping the Trust Terrain: LLMs in Software Engineering - Insights and Perspectives. - Dipin Khati; Yijin Liu; David N. Palacio; Yixuan Zhang; Denys Poshyvanyk (CoRR, 2025)

On Explaining (Large) Language Models For Code Using Global Code-Based Explanations. - David N. Palacio; Dipin Khati; Daniel Rodríguez-Cárdenas; Alejandro Velasco; Denys Poshyvanyk (CoRR, 2025)

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering. - David N. Palacio (CoRR, 2025)