Showing cs.SEShow all
3 papers · 1 filter
cs.SE2025
Deep Learning-based Code Completion: On the Impact on Performance of Contextual Information
Matteo Ciniselli, Luca Pascarella, Gabriele Bavota
Code completion aims at speeding up code writing by recommending to developers the next tokens they are likely to type. Deep Learning (DL) models pushed the boundaries of code comp…
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.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…