Detecting code smells using machine learning techniques: Are we there yet? D Di Nucci, F Palomba, DA Tamburri, A Serebrenik, A De Lucia 2018 ieee 25th international conference on software analysis, evolution and …, 2018 | 272 | 2018 |
A developer centered bug prediction model D Di Nucci, F Palomba, G De Rosa, G Bavota, R Oliveto, A De Lucia IEEE Transactions on Software Engineering 44 (1), 5-24, 2017 | 146 | 2017 |
On the impact of code smells on the energy consumption of mobile applications F Palomba, D Di Nucci, A Panichella, A Zaidman, A De Lucia Information and Software Technology, 2018 | 126 | 2018 |
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 | 124 | 2019 |
Lightweight detection of android-specific code smells: The adoctor project F Palomba, D Di Nucci, A Panichella, A Zaidman, A De Lucia 2017 IEEE 24th international conference on software analysis, evolution and …, 2017 | 120 | 2017 |
Software-based energy profiling of android apps: Simple, efficient and reliable? D Di Nucci, F Palomba, A Prota, A Panichella, A Zaidman, A De Lucia 2017 IEEE 24th international conference on software analysis, evolution and …, 2017 | 117 | 2017 |
On the diffusion of test smells in automatically generated test code: An empirical study F Palomba, D Di Nucci, A Panichella, R Oliveto, A De Lucia Proceedings of the 9th international workshop on search-based software …, 2016 | 104 | 2016 |
Dynamic selection of classifiers in bug prediction: An adaptive method D Di Nucci, F Palomba, R Oliveto, A De Lucia IEEE Transactions on Emerging Topics in Computational Intelligence 1 (3 …, 2017 | 83 | 2017 |
Landfill: An open dataset of code smells with public evaluation F Palomba, D Di Nucci, M Tufano, G Bavota, R Oliveto, D Poshyvanyk, ... 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories, 482-485, 2015 | 81 | 2015 |
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 | 75 | 2020 |
A graph-based dataset of commit history of real-world android apps FX Geiger, I Malavolta, L Pascarella, F Palomba, D Di Nucci, A Bacchelli Proceedings of the 15th international conference on mining software …, 2018 | 69 | 2018 |
Cross-project just-in-time bug prediction for mobile apps: An empirical assessment G Catolino, D Di Nucci, F Ferrucci 2019 IEEE/ACM 6th International Conference on Mobile Software Engineering …, 2019 | 65 | 2019 |
A test case prioritization genetic algorithm guided by the hypervolume indicator D Di Nucci, A Panichella, A Zaidman, A De Lucia IEEE Transactions on Software Engineering 46 (6), 674-696, 2018 | 64 | 2018 |
Toward a catalog of software quality metrics for infrastructure code S Dalla Palma, D Di Nucci, F Palomba, DA Tamburri Journal of Systems and Software 170, 110726, 2020 | 62 | 2020 |
Within-project defect prediction of infrastructure-as-code using product and process metrics S Dalla Palma, D Di Nucci, F Palomba, DA Tamburri IEEE Transactions on Software Engineering 48 (6), 2086-2104, 2021 | 60 | 2021 |
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 | 59 | 2019 |
Petra: a software-based tool for estimating the energy profile of android applications D Di Nucci, F Palomba, A Prota, A Panichella, A Zaidman, A De Lucia 2017 IEEE/ACM 39th International Conference on Software Engineering …, 2017 | 56 | 2017 |
Scented since the beginning: On the diffuseness of test smells in automatically generated test code G Grano, F Palomba, D Di Nucci, A De Lucia, HC Gall Journal of Systems and Software 156, 312-327, 2019 | 49 | 2019 |
Do developers update third-party libraries in mobile apps? P Salza, F Palomba, D Di Nucci, C D'Uva, A De Lucia, F Ferrucci Proceedings of the 26th conference on program comprehension, 255-265, 2018 | 49 | 2018 |
The do’s and don’ts of infrastructure code: A systematic gray literature review I Kumara, M Garriga, AU Romeu, D Di Nucci, F Palomba, DA Tamburri, ... Information and Software Technology 137, 106593, 2021 | 46 | 2021 |