1 citations · 1 across the 2 of their papers we have counts for
4 papers
Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization
Alessandro Midolo, Alessandro Giagnorio, Fiorella Zampetti +3
Large Language Models (LLMs) are nowadays extensively used for various types of software engineering tasks, primarily code generation. Previous research has shown how suitable prom…
Why Personalizing Deep Learning-Based Code Completion Tools Matters
Alessandro Giagnorio, Alberto Martin-Lopez, Gabriele Bavota
Deep learning (DL)-based code completion tools have transformed software development by enabling advanced code generation. These tools leverage models trained on vast amounts of co…
Evaluating the Impact of Post-Training Quantization on Large Language Models for Code Generation
Alessandro Giagnorio, Antonio Mastropaolo, Saima Afrin +2
Large Language Models (LLMs) have shown an impressive capability in code generation. The LLM effectiveness generally increases with its size: The higher the number of LLM's trainab…
Enhancing Code Generation for Low-Resource Languages: No Silver Bullet
Alessandro Giagnorio, Alberto Martin-Lopez, Gabriele Bavota
The advent of Large Language Models (LLMs) has significantly advanced the field of automated code generation. LLMs rely on large and diverse datasets to learn syntax, semantics, an…