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ORCIDDavid Nader-Palacio
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2020 – today
- 2024
[j3]David Nader-Palacio
, Alejandro Velasco, Nathan Cooper, Alvaro Rodriguez, Kevin Moran, Denys Poshyvanyk:
Toward a Theory of Causation for Interpreting Neural Code Models. IEEE Trans. Software Eng. 50(5): 1215-1243 (2024)- [c6]Alejandro Velasco, David N. Palacio, Daniel Rodríguez-Cárdenas, Denys Poshyvanyk:
Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code? NIER@ICSE 2024: 72-76 - [i10]Alejandro Velasco, David N. Palacio, Daniel Rodríguez-Cárdenas, Denys Poshyvanyk:
Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code? CoRR abs/2401.01512 (2024) - [i9]David N. Palacio, Daniel Rodríguez-Cárdenas, Alejandro Velasco, Dipin Khati, Kevin Moran, Denys Poshyvanyk:
Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations. CoRR abs/2407.08983 (2024) - 2023
- [j2]Antonio Mastropaolo, Nathan Cooper, David Nader-Palacio, Simone Scalabrino, Denys Poshyvanyk, Rocco Oliveto, Gabriele Bavota:
Using Transfer Learning for Code-Related Tasks. IEEE Trans. Software Eng. 49(4): 1580-1598 (2023) - [c5]Daniel Rodríguez-Cárdenas, David N. Palacio, Dipin Khati, Henry Burke, Denys Poshyvanyk:
Benchmarking Causal Study to Interpret Large Language Models for Source Code. ICSME 2023: 329-334 - [i8]David N. Palacio, Nathan Cooper, Alvaro Rodriguez, Kevin Moran, Denys Poshyvanyk:
Toward a Theory of Causation for Interpreting Neural Code Models. CoRR abs/2302.03788 (2023) - [i7]David N. Palacio, Alejandro Velasco, Daniel Rodríguez-Cárdenas, Kevin Moran, Denys Poshyvanyk:
Evaluating and Explaining Large Language Models for Code Using Syntactic Structures. CoRR abs/2308.03873 (2023) - [i6]Daniel Rodríguez-Cárdenas, David N. Palacio, Dipin Khati, Henry Burke, Denys Poshyvanyk:
Benchmarking Causal Study to Interpret Large Language Models for Source Code. CoRR abs/2308.12415 (2023) - 2022
- [j1]Cody Watson, Nathan Cooper, David Nader-Palacio, Kevin Moran, Denys Poshyvanyk:
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research. ACM Trans. Softw. Eng. Methodol. 31(2): 32:1-32:58 (2022) - [d1]Antonio Mastropaolo, Simone Scalabrino, Nathan Cooper, David Nader-Palacio, Denys Poshyvanyk, Rocco Oliveto, Gabriele Bavota:
Replication Package for: Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks. Zenodo, 2022
- [i5]Antonio Mastropaolo, Nathan Cooper, David Nader-Palacio, Simone Scalabrino, Denys Poshyvanyk, Rocco Oliveto, Gabriele Bavota:
Using Transfer Learning for Code-Related Tasks. CoRR abs/2206.08574 (2022) - 2021
- [c4]Antonio Mastropaolo, Simone Scalabrino, Nathan Cooper, David Nader-Palacio, Denys Poshyvanyk, Rocco Oliveto, Gabriele Bavota:
Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks. ICSE 2021: 336-347 - [i4]Antonio Mastropaolo, Simone Scalabrino, Nathan Cooper, David Nader-Palacio, Denys Poshyvanyk, Rocco Oliveto, Gabriele Bavota:
Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks. CoRR abs/2102.02017 (2021) - 2020
- [c3]Kevin Moran, David N. Palacio, Carlos Bernal-Cárdenas, Daniel McCrystal, Denys Poshyvanyk, Chris Shenefiel, Jeff Johnson:
Improving the effectiveness of traceability link recovery using hierarchical bayesian networks. ICSE 2020: 873-885 - [i3]Kevin Moran, David N. Palacio, Carlos Bernal-Cárdenas, Daniel McCrystal, Denys Poshyvanyk, Chris Shenefiel, Jeff Johnson:
Improving the Effectiveness of Traceability Link Recovery using Hierarchical Bayesian Networks. CoRR abs/2005.09046 (2020) - [i2]Cody Watson, Nathan Cooper, David Nader-Palacio, Kevin Moran, Denys Poshyvanyk:
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research. CoRR abs/2009.06520 (2020)
2010 – 2019
- 2019
- [c2]David N. Palacio, Daniel McCrystal, Kevin Moran, Carlos Bernal-Cárdenas, Denys Poshyvanyk, Chris Shenefiel:
Learning to Identify Security-Related Issues Using Convolutional Neural Networks. ICSME 2019: 140-144 - [i1]David N. Palacio, Daniel McCrystal, Kevin Moran, Carlos Bernal-Cárdenas, Denys Poshyvanyk, Chris Shenefiel:
Learning to Identify Security-Related Issues Using Convolutional Neural Networks. CoRR abs/1908.00614 (2019) - 2018
- [c1]David Nader-Palacio, Daniel Rodríguez-Cárdenas, Jonatan Gómez:
Assessing single-objective performance convergence and time complexity for refactoring detection. GECCO (Companion) 2018: 1606-1613
Coauthor Index
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last updated on 2024-10-10 22:16 CEST by the dblp team
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