1 citations · 1 across the 9 of their papers we have counts for
9 papers
Machine Learning in the Wild: Early Evidence of Non-Compliant ML-Automation in Open-Source Software
Zohaib Arshid, Daniele Bifolco, Fiorella Zampetti +1
The increasing availability of Machine Learning (ML) models, particularly foundation models, enables their use across a range of downstream applications, from scenarios with missin…
Developers and Generative AI: A Study of Self-Admitted Usage in Open Source Projects
Rosalia Tufano, Federica Pepe, Fiorella Zampetti +4
The availability of generative Artificial Intelligence (AI) tools such as ChatGPT or GitHub Copilot is reshaping the way in which software is developed, evolved, and maintained. Of…
How are MLOps Frameworks Used in Open Source Projects? An Empirical Characterization
Fiorella Zampetti, Federico Stocchetti, Federica Razzano +2
Machine Learning (ML) Operations (MLOps) frameworks have been conceived to support developers and AI engineers in managing the lifecycle of their ML models. While such frameworks p…
Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization
Alessandro Midolo, Alessandro Giagnorio, Fiorella Zampetti +3
Large Language Models (LLMs) are nowadays extensively used for various types of software engineering tasks, primarily code generation. Previous research has shown how suitable prom…
CodeGenLink: A Tool to Find the Likely Origin and License of Automatically Generated Code
Daniele Bifolco, Guido Annicchiarico, Pierluigi Barbiero +2
Large Language Models (LLMs) are widely used in software development tasks nowadays. Unlike reusing code taken from the Web, for LLMs' generated code, developers are concerned abou…
Do LLMs Provide Links to Code Similar to what they Generate? A Study with Gemini and Bing CoPilot
Daniele Bifolco, Pietro Cassieri, Giuseppe Scanniello +2
Large Language Models (LLMs) are currently used for various software development tasks, including generating code snippets to solve specific problems. Unlike reuse from the Web, LL…
A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems
Federica Pepe, Fiorella Zampetti, Antonio Mastropaolo +2
The development of Machine Learning (ML)- and, more recently, of Deep Learning (DL)-intensive systems requires suitable choices, e.g., in terms of technology, algorithms, and hyper…
Why Developers Refactor Source Code: A Mining-based Study
Jevgenija Pantiuchina, Fiorella Zampetti, Simone Scalabrino +4
Refactoring aims at improving code non-functional attributes without modifying its external behavior. Previous studies investigated the motivations behind refactoring by surveying…
On the Relationship between Refactoring Actions and Bugs: A Differentiated Replication
Massimiliano Di Penta, Gabriele Bavota, Fiorella Zampetti
Software refactoring aims at improving code quality while preserving the system's external behavior. Although in principle refactoring is a behavior-preserving activity, a study pr…