5 papers
On the Impact of Code Comments for Automated Bug-Fixing: An Empirical Study
Antonio Vitale, Emanuela Guglielmi, Simone Scalabrino +1
Large Language Models (LLMs) are increasingly relevant in Software Engineering research and practice, with Automated Bug Fixing (ABF) being one of their key applications. ABF invol…
Human-Agent versus Human Pull Requests: A Testing-Focused Characterization and Comparison
Roberto Milanese, Francesco Salzano, Angelica Spina +4
AI-based coding agents are increasingly integrated into software development workflows, collaborating with developers to create pull requests (PRs). Despite their growing adoption,…
Toward Explaining Large Language Models in Software Engineering Tasks
Antonio Vitale, Khai-Nguyen Nguyen, Denys Poshyvanyk +3
Recent progress in Large Language Models (LLMs) has substantially advanced the automation of software engineering (SE) tasks, enabling complex activities such as code generation an…
Personalized Code Readability Assessment: Are We There Yet?
Antonio Vitale, Emanuela Guglielmi, Rocco Oliveto +1
Unreadable code could be a breeding ground for errors. Thus, previous work defined approaches based on machine learning to automatically assess code readability that can warn devel…
Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough?
Antonio Vitale, Antonio Mastropaolo, Rocco Oliveto +2
Automated code summarization is a long-standing goal for code comprehension. This task automatically generates documentation using a given method. Deep Learning (DL)-based approach…