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Marco Castelluccio
Marco Castelluccio
Università Federico II di Napoli and Mozilla
Email verificata su unina.it
Titolo
Citata da
Citata da
Anno
Land use classification in remote sensing images by convolutional neural networks
M Castelluccio, G Poggi, C Sansone, L Verdoliva
arXiv preprint arXiv:1508.00092, 2015
7892015
Understanding flaky tests: The developer’s perspective
M Eck, F Palomba, M Castelluccio, A Bacchelli
Proceedings of the 2019 27th ACM Joint Meeting on European Software …, 2019
1502019
What makes a code change easier to review: an empirical investigation on code change reviewability
A Ram, AA Sawant, M Castelluccio, A Bacchelli
Proceedings of the 2018 26th ACM Joint Meeting on European Software …, 2018
492018
Automatically analyzing groups of crashes for finding correlations
M Castelluccio, C Sansone, L Verdoliva, G Poggi
Proceedings of the 2017 11th Joint Meeting on Foundations of Software …, 2017
272017
Training convolutional neural networks for semantic classification of remote sensing imagery
M Castelluccio, G Poggi, C Sansone, L Verdoliva
2017 Joint Urban Remote Sensing Event (JURSE), 1-4, 2017
232017
An empirical study of patch uplift in rapid release development pipelines
M Castelluccio, L An, F Khomh
Empirical Software Engineering 24, 3008-3044, 2019
192019
Why are some bugs non-reproducible?:–an empirical investigation using data fusion–
MM Rahman, F Khomh, M Castelluccio
2020 IEEE international conference on software maintenance and evolution …, 2020
182020
Land use classification in remote sensing images by convolutional neural networks, 2015
M Castelluccio, G Poggi, C Sansone, L Verdoliva
arXiv preprint arXiv:1508.00092, 0
16
rust-code-analysis: A rust library to analyze and extract maintainability information from source codes
L Ardito, L Barbato, M Castelluccio, R Coppola, C Denizet, S Ledru, ...
SoftwareX 12, 100635, 2020
142020
SZZ in the time of pull requests
F Petrulio, D Ackermann, E Fregnan, G Calikli, M Castelluccio, S Ledru, ...
arXiv preprint arXiv:2209.03311, 2022
72022
Is it safe to uplift this patch?: An empirical study on mozilla firefox
M Castelluccio, L An, F Khomh
2017 IEEE international conference on software maintenance and evolution …, 2017
72017
An empirical study of dll injection bugs in the firefox ecosystem
L An, M Castelluccio, F Khomh
Empirical Software Engineering 24, 1799-1822, 2019
52019
Land use classification. n
M Castelluccio, G Poggi, C Sansone, L Verdoliva
Remote Sensing Images by Convolutional Neural Networks, 0
5
Works for me! cannot reproduce–a large scale empirical study of non-reproducible bugs
MM Rahman, F Khomh, M Castelluccio
Empirical Software Engineering 27 (5), 111, 2022
42022
Why did this reviewed code crash? An empirical study of mozilla firefox
L An, F Khomh, S Mcintosh, M Castelluccio
2018 25th Asia-Pacific Software Engineering Conference (APSEC), 396-405, 2018
32018
Mind the Gap: What Working With Developers on Fuzz Tests Taught Us About Coverage Gaps
C Brandt, M Castelluccio, C Holler, J Kratzer, A Zaidman, A Bacchelli
Proceedings of the International Conference on Software Engineering-Software …, 2024
22024
Data and Material for “What Makes A Code Change Easier To Review?”
A Ram, AA Sawant, M Castelluccio, A Bacchelli
22018
What Makes a Code Change Easier to Review
A Ram, AA Sawant, M Castelluccio, A Bacchelli
An Empirical Investigation on Code Change Reviewability. ESEC/FSE. DOI …, 2018
22018
Predicting the Impact of Crashes Across Release Channels
S Mujahid, DE Costa, M Castelluccio
arXiv preprint arXiv:2401.13667, 2024
2024
Why are Some Bugs Non-Reproducible? An Empirical Investigation using Data Fusion
M Masudur Rahman, F Khomh, M Castelluccio
arXiv e-prints, arXiv: 2108.05316, 2021
2021
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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