| Studying the usage of text-to-text transfer transformer to support code-related tasks A Mastropaolo, S Scalabrino, N Cooper, DN Palacio, D Poshyvanyk, ... 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE …, 2021 | 281 | 2021 |
| A systematic literature review on the use of deep learning in software engineering research C Watson, N Cooper, DN Palacio, K Moran, D Poshyvanyk ACM Transactions on Software Engineering and Methodology (TOSEM) 31 (2), 1-58, 2022 | 142 | 2022 |
| Using transfer learning for code-related tasks A Mastropaolo, N Cooper, DN Palacio, S Scalabrino, D Poshyvanyk, ... IEEE Transactions on Software Engineering 49 (4), 1580-1598, 2022 | 67 | 2022 |
| Improving the effectiveness of traceability link recovery using hierarchical bayesian networks K Moran, DN Palacio, C Bernal-Cárdenas, D McCrystal, D Poshyvanyk, ... Proceedings of the ACM/IEEE 42nd International Conference on Software …, 2020 | 46 | 2020 |
| Learning to identify security-related issues using convolutional neural networks DN Palacio, D McCrystal, K Moran, C Bernal-Cárdenas, D Poshyvanyk, ... 2019 IEEE International conference on software maintenance and evolution …, 2019 | 27 | 2019 |
| Toward a theory of causation for interpreting neural code models DN Palacio, A Velasco, N Cooper, A Rodriguez, K Moran, D Poshyvanyk IEEE Transactions on Software Engineering, 2024 | 20 | 2024 |
| Benchmarking causal study to interpret large language models for source code D Rodriguez-Cardenas, DN Palacio, D Khati, H Burke, D Poshyvanyk 2023 IEEE International Conference on Software Maintenance and Evolution …, 2023 | 20 | 2023 |
| Evaluating and explaining large language models for code using syntactic structures DN Palacio, A Velasco, D Rodriguez-Cardenas, K Moran, D Poshyvanyk arXiv preprint arXiv:2308.03873, 2023 | 13 | 2023 |
| Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations DN Palacio, D Rodriguez-Cardenas, A Velasco, D Khati, K Moran, ... arXiv preprint arXiv:2407.08983, 2024 | 6 | 2024 |
| Which syntactic capabilities are statistically learned by masked language models for code? A Velasco, DN Palacio, D Rodriguez-Cardenas, D Poshyvanyk Proceedings of the 2024 ACM/IEEE 44th International Conference on Software …, 2024 | 4 | 2024 |
| Assessing single-objective performance convergence and time complexity for refactoring detection D Nader-Palacio, D Rodríguez-Cárdenas, J Gomez Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2018 | 4 | 2018 |
| Toward Neurosymbolic Program Comprehension A Velasco, A Garryyeva, DN Palacio, A Mastropaolo, D Poshyvanyk arXiv preprint arXiv:2502.01806, 2025 | | 2025 |
| How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study A Velasco, D Rodriguez-Cardenas, DN Palacio, LR Alif, D Poshyvanyk arXiv preprint arXiv:2412.18989, 2024 | | 2024 |
| On Interpreting the Effectiveness of Unsupervised Software Traceability with Information Theory DN Palacio, D Rodriguez-Cardenas, D Poshyvanyk, K Moran arXiv preprint arXiv:2412.04704, 2024 | | 2024 |
| Perspective of Software Engineering Researchers on Machine Learning Practices Regarding Research, Review, and Education A Mojica-Hanke, DN Palacio, D Poshyvanyk, M Linares-Vásquez, ... arXiv preprint arXiv:2411.19304, 2024 | | 2024 |
| Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We? C O'Brien, D Rodriguez-Cardenas, A Velasco, DN Palacio, D Poshyvanyk arXiv preprint arXiv:2411.17927, 2024 | | 2024 |
| A computational solution for the software refactoring problem: a formalism toward an optimization approach DA Nader Palacio Departamento de Ingeniería de Sistemas e Industrial, 2017 | | 2017 |
| ICSME 2023 A Awal, AI Alam, AV Dimate, A Saha, B Nwiran, DHR Cardenas, ... | | |
| Subreviewers of MOBILESoft 2020 AS Ami, CEB Cardenas, EM Grua, S Mohian, DN Palacio, GL Scoccia, ... | | |
| Additional Reviewers MSR 2019 A Parsai, A Brito, A Hora, A Di Sorbo, B van Bladel, CB Cardenas, ... | | |
Denys PoshyvanykChancellor Professor of Computer Science, William & MaryПотвърден имейл адрес: cs.wm.edu