activity
20192025
most citedAn Empirical Study on the Usage of Transformer Models for Code Completion

98 citations · 122 across the 16 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.SE2022

Automated Variable Renaming: Are We There Yet?

Antonio Mastropaolo, Emad Aghajani, Luca Pascarella +1

Identifiers, such as method and variable names, form a large portion of source code. Therefore, low-quality identifiers can substantially hinder code comprehension. To support deve…

cs.SE2022

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…

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.SE2022★ 3 cited

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…

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…