86 citations · 103 across the 6 of their papers we have counts for
12 papers · 1 filter
Automated Refactoring of Non-Idiomatic Python Code: A Differentiated Replication with LLMs
Alessandro Midolo, Massimiliano Di Penta
In the Python ecosystem, the adoption of idiomatic constructs has been fostered because of their expressiveness, increasing productivity and even efficiency, despite controversial…
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…
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…
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…
DeepMutation: A Neural Mutation Tool
Michele Tufano, Jason Kimko, Shiya Wang +4
Mutation testing can be used to assess the fault-detection capabilities of a given test suite. To this aim, two characteristics of mutation testing frameworks are of paramount impo…