most citedMachine Learning-Based Test Smell Detection

1 citations · 1 across the 1 of their papers we have counts for

collaborators

6 papers

cs.SE20251 cited

Teaching Mining Software Repositories

Zadia Codabux, Fatemeh Fard, Roberto Verdecchia +3

Mining Software Repositories (MSR) has become a popular research area recently. MSR analyzes different sources of data, such as version control systems, code repositories, defect t…

cs.SE20243 cited

Reformulating Regression Test Suite Optimization using Quantum Annealing -- an Empirical Study

Antonio Trovato, Manuel De Stefano, Fabiano Pecorelli +2

Maintaining software quality is crucial in the dynamic landscape of software development. Regression testing ensures that software works as expected after changes are implemented.…

cs.SE20243 cited

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…

cs.SE20241 cited

Architectural Design Decisions for Self-Serve Data Platforms in Data Meshes

Tom van Eijk, Indika Kumara, Dario Di Nucci +2

Data mesh is an emerging decentralized approach to managing and generating value from analytical enterprise data at scale. It shifts the ownership of the data to the business domai…

cs.SE20234 cited

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…

cs.SE20221 cited

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…