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Fabiano Pecorelli
Fabiano Pecorelli
Postdoctoral Researcher, JADS, Tu/e
Verified email at jads.nl - Homepage
Title
Cited by
Cited by
Year
Comparing heuristic and machine learning approaches for metric-based code smell detection
F Pecorelli, F Palomba, D Di Nucci, A De Lucia
2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC …, 2019
1182019
A large empirical assessment of the role of data balancing in machine-learning-based code smell detection
F Pecorelli, D Di Nucci, C De Roover, A De Lucia
Journal of Systems and Software 169, 110693, 2020
692020
Developer-driven code smell prioritization
F Pecorelli, F Palomba, F Khomh, A De Lucia
Proceedings of the 17th International Conference on Mining Software …, 2020
572020
On the role of data balancing for machine learning-based code smell detection
F Pecorelli, D Di Nucci, C De Roover, A De Lucia
Proceedings of the 3rd ACM SIGSOFT international workshop on machine …, 2019
522019
Just-in-time test smell detection and refactoring: The darts project
S Lambiase, A Cupito, F Pecorelli, A De Lucia, F Palomba
Proceedings of the 28th international conference on program comprehension …, 2020
312020
Software engineering for quantum programming: How far are we?
M De Stefano, F Pecorelli, D Di Nucci, F Palomba, A De Lucia
Journal of Systems and Software 190, 111326, 2022
272022
Testing of mobile applications in the wild: A large-scale empirical study on android apps
F Pecorelli, G Catolino, F Ferrucci, A De Lucia, F Palomba
Proceedings of the 28th international conference on program comprehension …, 2020
272020
Cloud continuum: The definition
S Moreschini, F Pecorelli, X Li, S Naz, D Hästbacka, D Taibi
IEEE Access 10, 131876-131886, 2022
242022
Splicing community patterns and smells: A preliminary study
M De Stefano, F Pecorelli, DA Tamburri, F Palomba, A De Lucia
Proceedings of the ieee/acm 42nd international conference on software …, 2020
242020
The relation of test-related factors to software quality: a case study on apache systems
F Pecorelli, F Palomba, A De Lucia
Empirical Software Engineering 26, 1-42, 2021
192021
A critical comparison on six static analysis tools: detection, agreement, and precision
V Lenarduzzi, S Lujan, N Saarimaki, F Palomba
arXiv preprint arXiv:2101.08832, 2021
192021
Refactoring android-specific energy smells: A plugin for android studio
E Iannone, F Pecorelli, D Di Nucci, F Palomba, A De Lucia
Proceedings of the 28th international conference on program comprehension …, 2020
192020
Comparing within-and cross-project machine learning algorithms for code smell detection
M De Stefano, F Pecorelli, F Palomba, A De Lucia
Proceedings of the 5th International Workshop on Machine Learning Techniques …, 2021
182021
A multivocal literature review of mlops tools and features
G Recupito, F Pecorelli, G Catolino, S Moreschini, D Di Nucci, F Palomba, ...
2022 48th Euromicro Conference on Software Engineering and Advanced …, 2022
152022
Vitrum: a plug-in for the visualization of test-related metrics
F Pecorelli, G Di Lillo, F Palomba, A De Lucia
Proceedings of the International Conference on Advanced Visual Interfaces, 1-3, 2020
142020
Software testing and Android applications: a large-scale empirical study
F Pecorelli, G Catolino, F Ferrucci, A De Lucia, F Palomba
Empirical Software Engineering 27 (2), 31, 2022
132022
Impacts of software community patterns on process and product: An empirical study
M De Stefano, E Iannone, F Pecorelli, DA Tamburri
Science of Computer Programming 214, 102731, 2022
132022
A preliminary study on the adequacy of static analysis warnings with respect to code smell prediction
S Lujan, F Pecorelli, F Palomba, A De Lucia, V Lenarduzzi
Proceedings of the 4th ACM SIGSOFT international workshop on machine …, 2020
132020
cASpER: A plug-in for automated code smell detection and refactoring
M De Stefano, MS Gambardella, F Pecorelli, F Palomba, A De Lucia
Proceedings of the International Conference on Advanced Visual Interfaces, 1-3, 2020
112020
Adaptive selection of classifiers for bug prediction: A large-scale empirical analysis of its performances and a benchmark study
F Pecorelli, D Di Nucci
Science of Computer Programming 205, 102611, 2021
102021
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