98 citations · 122 across the 16 of their papers we have counts for
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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…
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…
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…
On the Empirical Evidence of Microservice Logical Coupling. A Registered Report
Dario Amoroso d Aragona, Luca Pascarella, Andrea Janes +3
[Context] Coupling is a widely discussed metric by software engineers while developing complex software systems, often referred to as a crucial factor and symptom of a poor or good…
Breaks and Code Quality: Investigating the Impact of Forgetting on Software Development. A Registered Report
Dario Amoroso d'Aragona, Luca Pascarella, Andrea Janes +3
Developers interrupting their participation in a project might slowly forget critical information about the code, such as its intended purpose, structure, the impact of external de…
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…
Automating Code-Related Tasks Through Transformers: The Impact of Pre-training
Rosalia Tufano, Luca Pascarella, Gabriele Bavota
Transformers have gained popularity in the software engineering (SE) literature. These deep learning models are usually pre-trained through a self-supervised objective, meant to pr…
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…
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…
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 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 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…
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…
Evaluating SZZ Implementations Through a Developer-informed Oracle
Giovanni Rosa, Luca Pascarella, Simone Scalabrino +4
The SZZ algorithm for identifying bug-inducing changes has been widely used to evaluate defect prediction techniques and to empirically investigate when, how, and by whom bugs are…
Towards Automating Code Review Activities
Rosalia Tufano, Luca Pascarella, Michele Tufano +2
Code reviews are popular in both industrial and open source projects. The benefits of code reviews are widely recognized and include better code quality and lower likelihood of int…
On The Effect Of Code Review On Code Smells
Luca Pascarella, Davide Spadini, Fabio Palomba +1
Code smells are symptoms of poor design quality. Since code review is a process that also aims at improving code quality, we investigate whether and how code review influences the…