Fault injection in the process descriptor of a Unix-based operating system. - Bartolomeo Montrucchio; Maurizio Rebaudengo; Alejandro Velasco (DFT, 2014)

Software-implemented fault injection in operating system kernel mutex data structure. - Bartolomeo Montrucchio; Maurizio Rebaudengo; Alejandro David Velasco (LASCAS, 2014)

On gait recognition with smartphone accelerometer. - Renato Ferrero; Filippo Gandino; Bartolomeo Montrucchio; Maurizio Rebaudengo; Alejandro Velasco; Imane Benkhelifa (MECO, 2015)

KITO tool: A fault injection environment in Linux kernel data structures. - Alejandro Velasco; Bartolomeo Montrucchio; Maurizio Rebaudengo (Microelectron. Reliab., 2016)

A Mobile and Low-Cost System for Environmental Monitoring: A Case Study. - Alejandro Velasco; Renato Ferrero; Filippo Gandino; Bartolomeo Montrucchio; Maurizio Rebaudengo (Sensors, 2016)

Evolving a Project-Based Software Engineering Course: A Case Study. - David Delgado 002; Alejandro Velasco; Jairo Aponte; Andrian Marcus (CSEE&T, 2017)

TMR technique for mutex kernel data structures. - Alejandro David Velasco; Bartolomeo Montrucchio; Maurizio Rebaudengo (LATS, 2017)

Software-based methods for Operating system dependability. - Alejandro Velasco (2017)

Recovering Fine Grained Traceability Links Between Software Mandatory Constraints and Source Code. - Alejandro Velasco; Jairo Hernan Aponte Melo (ICAI, 2019)

Applying Machine Learning with Chaos Engineering. - Juan Hernández-Serrato; Alejandro Velasco; Yury Nifio; Mario Linares-Vásquez (ISSRE Workshops, 2020)

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)

Beyond Accuracy: Evaluating Source Code Capabilities in Large Language Models for Software Engineering. - Alejandro Velasco (ICSE Companion, 2024)

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)

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)

SnipGen: A Mining Repository Framework for Evaluating LLMs for Code. - Daniel Rodríguez-Cárdenas; Alejandro Velasco; Denys Poshyvanyk (MSR, 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)

On the Effectiveness of LLM-as-a-judge for Code Generation and Summarization. - Giuseppe Crupi; Rosalia Tufano; Alejandro Velasco; Antonio Mastropaolo; Denys Poshyvanyk; Gabriele Bavota (CoRR, 2025)