43 citations · 56 across the 8 of their papers we have counts for
8 papers
When Code Smells Meet ML: On the Lifecycle of ML-specific Code Smells in ML-enabled Systems
Gilberto Recupito, Giammaria Giordano, Filomena Ferrucci +2
Context. The adoption of Machine Learning (ML)--enabled systems is steadily increasing. Nevertheless, there is a shortage of ML-specific quality assurance approaches, possibly beca…
Test Code Refactoring Unveiled: Where and How Does It Affect Test Code Quality and Effectiveness?
Luana Martins, Valeria Pontillo, Heitor Costa +3
Context. Refactoring has been widely investigated in the past in relation to production code quality, yet still little is known on how developers apply refactoring on test code. Sp…
A systematic literature review on the code smells datasets and validation mechanisms
Morteza Zakeri-Nasrabadi, Saeed Parsa, Ehsan Esmaili +1
The accuracy reported for code smell-detecting tools varies depending on the dataset used to evaluate the tools. Our survey of 45 existing datasets reveals that the adequacy of a d…
The Quantum Frontier of Software Engineering: A Systematic Mapping Study
Manuel De Stefano, Fabiano Pecorelli, Dario Di Nucci +2
Context. Quantum computing is becoming a reality, and quantum software engineering (QSE) is emerging as a new discipline to enable developers to design and develop quantum programs…
Toward End-to-End MLOps Tools Map: A Preliminary Study based on a Multivocal Literature Review
Sergio Moreschi, Gilberto Recupito, Valentina Lenarduzzi +3
MLOps tools enable continuous development of machine learning, following the DevOps process. Different MLOps tools have been presented on the market, however, such a number of tool…
Machine Learning-Based Test Smell Detection
Valeria Pontillo, Dario Amoroso d'Aragona, Fabiano Pecorelli +3
Context: Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and…