98 citations · 122 across the 38 of their papers we have counts for
6 papers · 1 filter
A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems
Federica Pepe, Fiorella Zampetti, Antonio Mastropaolo +2
The development of Machine Learning (ML)- and, more recently, of Deep Learning (DL)-intensive systems requires suitable choices, e.g., in terms of technology, algorithms, and hyper…
The Rise and Fall(?) of Software Engineering
Antonio Mastropaolo, Camilo Escobar-Velásquez, Mario Linares-Vásquez
Over the last ten years, the realm of Artificial Intelligence (AI) has experienced an explosion of revolutionary breakthroughs, transforming what seemed like a far-off dream into a…
How the Training Procedure Impacts the Performance of Deep Learning-based Vulnerability Patching
Antonio Mastropaolo, Vittoria Nardone, Gabriele Bavota +1
Generative deep learning (DL) models have been successfully adopted for vulnerability patching. However, such models require the availability of a large dataset of patches to learn…
Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based Study
Rosalia Tufano, Antonio Mastropaolo, Federica Pepe +3
Large Language Models (LLMs) have gained significant attention in the software engineering community. Nowadays developers have the possibility to exploit these models through indus…
Towards Summarizing Code Snippets Using Pre-Trained Transformers
Antonio Mastropaolo, Matteo Ciniselli, Luca Pascarella +3
When comprehending code, a helping hand may come from the natural language comments documenting it that, unfortunately, are not always there. To support developers in such a scenar…
Code Review Automation: Strengths and Weaknesses of the State of the Art
Rosalia Tufano, Ozren Dabić, Antonio Mastropaolo +2
The automation of code review has been tackled by several researchers with the goal of reducing its cost. The adoption of deep learning in software engineering pushed the automatio…