UniSa - IRIS Institutional Research Information System

Sfoglia per Autore  

Opzioni
Mostrati risultati da 1 a 20 di 32
Titolo Data di pubblicazione Autore(i) File
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.
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.
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.
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
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.
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.
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.
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.
Splicing Community Patterns and Smells: A Preliminary Study 1-gen-2020 De Stefano, M.; Pecorelli, F.; Tamburri, D. A.; Palomba, F.; De Lucia, A.
Testing of mobile applications in the wild: A large-scale empirical study on android apps 1-gen-2020 Pecorelli, F.; Catolino, G.; Ferrucci, F.; De Lucia, A.; Palomba, F.
VITRuM: A Plug-In for the Visualization of Test-Related Metrics 1-gen-2020 Pecorelli, F.; Di Lillo, G.; Palomba, F.; De Lucia, A.
Developer-Driven Code Smell Prioritization 1-gen-2020 Pecorelli, F.; Palomba, F.; Khomh, F.; De Lucia, A.
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.
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.
The Relation of Test-Related Factors to Software Quality: A Case Study on Apache Systems 1-gen-2021 Pecorelli, Fabiano; Palomba, Fabio; DE LUCIA, Andrea
“There and Back Again?” On the Influence of Software Community Dispersion Over Productivity 1-gen-2022 Lambiase, Stefano; Catolino, Gemma; Pecorelli, Fabiano; Tamburri, Damian A.; Palomba, Fabio; Van Den Heuvel, Willem-Jan; Ferrucci, Filomena
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.
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
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.
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.
Mostrati risultati da 1 a 20 di 32
Legenda icone

  •  file ad accesso aperto
  •  file disponibili sulla rete interna
  •  file disponibili agli utenti autorizzati
  •  file disponibili solo agli amministratori
  •  file sotto embargo
  •  nessun file disponibile