98 citations · 122 across the 38 of their papers we have counts for
6 papers · 1 filter
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…
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…
Toward Automatically Completing GitHub Workflows
Antonio Mastropaolo, Fiorella Zampetti, Gabriele Bavota +1
Continuous integration and delivery (CI/CD) are nowadays at the core of software development. Their benefits come at the cost of setting up and maintaining the CI/CD pipeline, whic…
Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?
Antonio Mastropaolo, Massimiliano Di Penta, Gabriele Bavota
Upon evolving their software, organizations and individual developers have to spend a substantial effort to pay back technical debt, i.e., the fact that software is released in a s…
Automatically Generating Dockerfiles via Deep Learning: Challenges and Promises
Giovanni Rosa, Antonio Mastropaolo, Simone Scalabrino +2
Containerization allows developers to define the execution environment in which their software needs to be installed. Docker is the leading platform in this field, and developers t…
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…