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Pecorelli, Fabiano

Pecorelli, Fabiano  

Dipartimento di Informatica/DI  

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Risultati 1 - 20 di 31 (tempo di esecuzione: 0.046 secondi).
Titolo Data di pubblicazione Autore(i) File
A critical comparison on six static analysis tools: Detection, agreement, and precision 1-gen-2023 Lenarduzzi, Valentina; Pecorelli, Fabiano; Saarimaki, Nyyti; Lujan, Savanna; Palomba, Fabio
A large empirical assessment of the role of data balancing in machine-learning-based code smell detection 1-gen-2020 Pecorelli, F.; Di Nucci, D.; De Roover, C.; De Lucia, A.
A Multivocal Literature Review of MLOps Tools and Features 1-gen-2022 Recupito, Gilberto; Pecorelli, Fabiano; Catolino, Gemma; Moreschini, Sergio; Nucci, Dario Di; Palomba, Fabio; Tamburri, Damian A.
A preliminary evaluation on the relationship among architectural and test smells 1-gen-2022 De Stefano, M.; Pecorelli, F.; Di Nucci, D.; De Lucia, A.
A preliminary study on the adequacy of static analysis warnings with respect to code smell prediction 1-gen-2020 Lujan, S.; Pecorelli, F.; Palomba, F.; De Lucia, A.; Lenarduzzi, V.
Acceptance and Development of Quantum Computing in the Netherlands and Germany: Barriers and Remedies From a Multi-Stakeholder Perspective 1-gen-2024 Martens, J.; Kumara, I.; Di Nucci, D.; Pecorelli, F.; Monsieur, G.; Tamburri, D. A.; Van Den Heuvel, W. -J.
Adaptive selection of classifiers for bug prediction: A large-scale empirical analysis of its performances and a benchmark study 1-gen-2021 Pecorelli, F.; Di Nucci, D.
An Empirical Investigation Into the Influence of Software Communities’ Cultural and Geographical Dispersion on Productivity 1-gen-2024 Lambiase, S.; Catolino, G.; Pecorelli, F.; Tamburri, D. A.; Palomba, F.; van den Heuvel, W. -J.; Ferrucci, F.
CASpER: A Plug-in for Automated Code Smell Detection and Refactoring 1-gen-2020 De Stefano, M.; Gambardella, M. S.; Pecorelli, F.; Palomba, F.; De Lucia, A.
CATTO: Just-in-time Test Case Selection and Execution 1-gen-2022 D'Aragona, Da; Pecorelli, F; Romano, S; Scanniello, G; Baldassarre, Mt; Janes, A; Lenarduzzi, V
Comparing heuristic and machine learning approaches for metric-based code smell detection 1-gen-2019 Pecorelli, Fabiano; Palomba, F.; Di Nucci, D.; De Lucia, A.
Comparing within-and cross-project machine learning algorithms for code smell detection 1-gen-2021 De Stefano, M.; Pecorelli, F.; Palomba, F.; De Lucia, A.
Developer-Driven Code Smell Prioritization 1-gen-2020 Pecorelli, F.; Palomba, F.; Khomh, F.; De Lucia, A.
Just-in-time test smell detection and refactoring: The DARTS project 1-gen-2020 Lambiase, S.; Cupito, A.; Pecorelli, F.; De Lucia, A.; Palomba, F.
Machine learning-based test smell detection 1-gen-2024 Pontillo, V.; Amoroso d'Aragona, D.; Pecorelli, F.; Di Nucci, D.; Ferrucci, F.; Palomba, F.
On the adequacy of static analysis warnings with respect to code smell prediction 1-gen-2022 Pecorelli, F.; Lujan, S.; Lenarduzzi, V.; Palomba, F.; De Lucia, A.
On the role of data balancing for machine learning-based code smell detection 1-gen-2019 Pecorelli, F.; Di Nucci, D.; De Roover, C.; De Lucia, A.
Predicting The Energy Consumption Level of Java Classes in Android Apps: An Exploratory Analysis 1-gen-2022 Iannone, E.; De Stefano, M.; Pecorelli, F.; De Lucia, A.
Refactoring android-specific energy smells: A plugin for android studio 1-gen-2020 Iannone, E.; Pecorelli, F.; Di Nucci, D.; Palomba, F.; De Lucia, A.
Refactoring Recommendations Based on the Optimization of Socio-Technical Congruence 1-gen-2020 De Stefano, M.; Pecorelli, F.; Tamburri, D. A.; Palomba, F.; De Lucia, A