papers

Publications (21)

cs.SE2022

Using Reinforcement Learning for Load Testing of Video Games

Rosalia Tufano, Simone Scalabrino, Luca Pascarella +3

Different from what happens for most types of software systems, testing video games has largely remained a manual activity performed by human testers. This is mostly due to the con…

cs.SE2021

An Empirical Study on the Usage of BERT Models for Code Completion

Matteo Ciniselli, Nathan Cooper, Luca Pascarella +3

Code completion is one of the main features of modern Integrated Development Environments (IDEs). Its objective is to speed up code writing by predicting the next code token(s) the…

cs.SE2023

Source Code Recommender Systems: The Practitioners' Perspective

Matteo Ciniselli, Luca Pascarella, Emad Aghajani +3

The automatic generation of source code is one of the long-lasting dreams in software engineering research. Several techniques have been proposed to speed up the writing of new cod…

cs.SE2023

Log Statements Generation via Deep Learning: Widening the Support Provided to Developers

Antonio Mastropaolo, Valentina Ferrari, Luca Pascarella +1

Logging assists in monitoring events that transpire during the execution of software. Previous research has highlighted the challenges confronted by developers when it comes to log…

cs.SE2022

Using Deep Learning to Generate Complete Log Statements

Antonio Mastropaolo, Luca Pascarella, Gabriele Bavota

Logging is a practice widely adopted in several phases of the software lifecycle. For example, during software development log statements allow engineers to verify and debug the sy…

cs.SE2023

On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot

Antonio Mastropaolo, Luca Pascarella, Emanuela Guglielmi +4

Software engineering research has always being concerned with the improvement of code completion approaches, which suggest the next tokens a developer will likely type while coding…