4 citations · 10 across the 8 of their papers we have counts for
10 papers
To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set?
Matteo Ciniselli, Luca Pascarella, Gabriele Bavota
Deep Learning (DL) models have been widely used to support code completion. These models, once properly trained, can take as input an incomplete code component (e.g., an incomplete…
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…
Using Pre-Trained Models to Boost Code Review Automation
Rosalia Tufano, Simone Masiero, Antonio Mastropaolo +3
Code review is a practice widely adopted in open source and industrial projects. Given the non-negligible cost of such a process, researchers started investigating the possibility…
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…
An Empirical Study on Code Comment Completion
Antonio Mastropaolo, Emad Aghajani, Luca Pascarella +1
Code comments play a prominent role in program comprehension activities. However, source code is not always documented and code and comments not always co-evolve. To deal with thes…
Logging Practices with Mobile Analytics: An Empirical Study on Firebase
Julian Harty, Haonan Zhang, Lili Wei +3
Software logs are of great value in both industrial and open-source projects. Mobile analytics logging enables developers to collect logs remotely from their apps running on end us…