98 citations · 108 across the 8 of their papers we have counts for
10 papers
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…
On the Generalizability of Deep Learning-based Code Completion Across Programming Language Versions
Matteo Ciniselli, Alberto Martin-Lopez, Gabriele Bavota
Code completion is a key feature of Integrated Development Environments (IDEs), aimed at predicting the next tokens a developer is likely to write, helping them write code faster a…
Towards Summarizing Code Snippets Using Pre-Trained Transformers
Antonio Mastropaolo, Matteo Ciniselli, Luca Pascarella +3
When comprehending code, a helping hand may come from the natural language comments documenting it that, unfortunately, are not always there. To support developers in such a scenar…
Code Review Automation: Strengths and Weaknesses of the State of the Art
Rosalia Tufano, Ozren Dabić, Antonio Mastropaolo +2
The automation of code review has been tackled by several researchers with the goal of reducing its cost. The adoption of deep learning in software engineering pushed the automatio…
Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization
Antonio Mastropaolo, Matteo Ciniselli, Massimiliano Di Penta +1
Several code summarization techniques have been proposed in the literature to automatically document a code snippet or a function. Ideally, software developers should be involved i…
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…
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…
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…
An Empirical Study on the Usage of Transformer Models for Code Completion
Matteo Ciniselli, Nathan Cooper, Luca Pascarella +5
Code completion aims at speeding up code writing by predicting the next code token(s) the developer is likely to write. Works in this field focused on improving the accuracy of the…
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…