| 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 | 141 | 2019 |
| 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 | 92 | 2020 |
| Developer-driven code smell prioritization F Pecorelli, F Palomba, F Khomh, A De Lucia Proceedings of the 17th International Conference on Mining Software …, 2020 | 74 | 2020 |
| Cloud continuum: The definition S Moreschini, F Pecorelli, X Li, S Naz, D Hästbacka, D Taibi IEEE Access 10, 131876-131886, 2022 | 69 | 2022 |
| 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 | 67 | 2022 |
| 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 | 67 | 2019 |
| A critical comparison on six static analysis tools: Detection, agreement, and precision V Lenarduzzi, F Pecorelli, N Saarimaki, S Lujan, F Palomba Journal of Systems and Software 198, 111575, 2023 | 54 | 2023 |
| 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 | 44 | 2022 |
| 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 | 41 | 2020 |
| 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 | 34 | 2020 |
| 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 | 28 | 2020 |
| 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 | 25 | 2022 |
| 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 | 25 | 2021 |
| 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 | 25 | 2020 |
| 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 | 24 | 2021 |
| 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 | 18 | 2020 |
| 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 | 15 | 2022 |
| 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 2020 International Conference on Advanced Visual …, 2020 | 15 | 2020 |
| On the adequacy of static analysis warnings with respect to code smell prediction F Pecorelli, S Lujan, V Lenarduzzi, F Palomba, A De Lucia Empirical Software Engineering 27 (3), 64, 2022 | 14 | 2022 |
| 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 | 14 | 2021 |
Fabio PalombaUniversity of Salerno, ItalyVahvistettu sähköpostiosoite verkkotunnuksessa unisa.it