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2020 – today
- 2025
[j6]Luigi Quaranta
, Kelly Azevedo, Fabio Calefato, Marcos Kalinowski:
A multivocal literature review on the benefits and limitations of industry-leading AutoML tools. Inf. Softw. Technol. 178: 107608 (2025)- 2024
- [j5]Fabio Calefato, Luigi Quaranta, Filippo Lanubile:
A lot of talk and a badge: An exploratory analysis of personal achievements in GitHub. Inf. Softw. Technol. 176: 107561 (2024) - [j4]Giuseppe Colavito, Filippo Lanubile, Nicole Novielli, Luigi Quaranta:
Impact of data quality for automatic issue classification using pre-trained language models. J. Syst. Softw. 210: 111838 (2024) - [j3]Filippo Lanubile, Silverio Martínez-Fernández, Luigi Quaranta:
Training Future Machine Learning Engineers: A Project-Based Course on MLOps. IEEE Softw. 41(2): 60-67 (2024) - [j2]Luigi Quaranta, Fabio Calefato, Filippo Lanubile:
Pynblint: A quality assurance tool to improve the quality of Python Jupyter notebooks. SoftwareX 28: 101959 (2024) - [c18]Giulio Mallardi, Fabio Calefato, Luigi Quaranta, Filippo Lanubile:
An MLOps Approach for Deploying Machine Learning Models in Healthcare Systems. BIBM 2024: 6832-6837 - [c17]Nicole Novielli, Rocco Oliveto, Fabio Palomba, Fabio Calefato, Giuseppe Colavito, Vincenzo De Martino, Antonio Della Porta, Giammaria Giordano, Emanuela Guglielmi, Filippo Lanubile, Luigi Quaranta, Gilberto Recupito, Simone Scalabrino, Angelica Spina, Antonio Vitale:
Continuous Quality Improvement of AI-based Systems: the QualAI Project. ESEM 2024: 603-607 - [c16]Giuseppe Colavito, Filippo Lanubile, Nicole Novielli, Luigi Quaranta:
Large Language Models for Issue Report Classification. Ital-IA 2024: 18-23 - [c15]Andrea Basile, Fabio Calefato, Filippo Lanubile, Giulio Mallardi, Luigi Quaranta:
An MLOps Solution Framework for Transitioning Machine Learning Models into eHealth Systems. Ital-IA 2024: 318-323 - [c14]Fabio Calefato, Filippo Lanubile, Luigi Quaranta:
Security Risks and Best Practices of MLOps: A Multivocal Literature Review. ITASEC 2024 - [c13]Giuseppe Colavito, Filippo Lanubile, Nicole Novielli, Luigi Quaranta:
Leveraging GPT-like LLMs to Automate Issue Labeling. MSR 2024: 469-480 - [c12]Nicole Novielli, Rocco Oliveto, Fabio Palomba, Fabio Calefato, Giuseppe Colavito, Vincenzo De Martino, Antonio Della Porta, Giammaria Giordano, Emanuela Guglielmi, Filippo Lanubile, Luigi Quaranta, Gilberto Recupito, Simone Scalabrino, Angelica Spina, Antonio Vitale:
QualAI: Continuous Quality Improvement of AI-based Systems. RCIS Workshops 2024 - [e1]Xavier Franch, Maya Daneva, Silverio Martínez-Fernández, Luigi Quaranta:
Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2024, Barcelona, Spain, October 24-25, 2024. ACM 2024, ISBN 979-8-4007-1047-6 [contents] - [d4]Kelly Azevedo, Luigi Quaranta, Fabio Calefato, Marcos Kalinowski:
Artifacts: A Multivocal Literature Review on the Benefits and Limitations of Automated Machine Learning Tools. Zenodo, 2024
- [d3]Kelly Azevedo, Luigi Quaranta, Fabio Calefato, Marcos Kalinowski:
Artifacts: A Multivocal Literature Review on the Benefits and Limitations of Automated Machine Learning Tools. Zenodo, 2024 - [i12]Kelly Azevedo, Luigi Quaranta, Fabio Calefato, Marcos Kalinowski:
A Multivocal Literature Review on the Benefits and Limitations of Automated Machine Learning Tools. CoRR abs/2401.11366 (2024) - 2023
- [c11]Fabio Calefato, Luigi Quaranta, Filippo Lanubile, Marcos Kalinowski:
Assessing the Use of AutoML for Data-Driven Software Engineering. ESEM 2023: 1-12 - [c10]Filippo Lanubile, Silverio Martínez-Fernández, Luigi Quaranta:
Teaching MLOps in Higher Education through Project-Based Learning. SEET@ICSE 2023: 95-100 - [i11]Filippo Lanubile, Silverio Martínez-Fernández, Luigi Quaranta:
Teaching MLOps in Higher Education through Project-Based Learning. CoRR abs/2302.01048 (2023) - [i10]Fabio Calefato, Luigi Quaranta, Filippo Lanubile:
A Lot of Talk and a Badge: An Empirical Analysis of Personal Achievements in GitHub. CoRR abs/2303.14702 (2023) - [i9]Fabio Calefato, Luigi Quaranta, Filippo Lanubile, Marcos Kalinowski:
Assessing the Use of AutoML for Data-Driven Software Engineering. CoRR abs/2307.10774 (2023) - 2022
- [j1]Luigi Quaranta, Fabio Calefato, Filippo Lanubile:
Eliciting Best Practices for Collaboration with Computational Notebooks. Proc. ACM Hum. Comput. Interact. 6(CSCW1): 87:1-87:41 (2022) - [c9]Luigi Quaranta, Fabio Calefato, Filippo Lanubile:
Pynblint: a static analyzer for Python Jupyter notebooks. CAIN 2022: 48-49 - [c8]Fabio Calefato, Filippo Lanubile, Luigi Quaranta:
A Preliminary Investigation of MLOps Practices in GitHub. ESEM 2022: 283-288 - [c7]Luigi Quaranta:
Assessing the Quality of Computational Notebooks for a Frictionless Transition from Exploration to Production. ICSE-Companion 2022: 256-260 - [i8]Luigi Quaranta, Fabio Calefato, Filippo Lanubile:
Eliciting Best Practices for Collaboration with Computational Notebooks. CoRR abs/2202.07233 (2022) - [i7]Luigi Quaranta, Fabio Calefato, Filippo Lanubile:
Pynblint: a Static Analyzer for Python Jupyter Notebooks. CoRR abs/2205.11934 (2022) - [i6]Luigi Quaranta:
Assessing the Quality of Computational Notebooks for a Frictionless Transition from Exploration to Production. CoRR abs/2205.11941 (2022) - [i5]Fabio Calefato, Filippo Lanubile, Luigi Quaranta:
A Preliminary Investigation of MLOps Practices in GitHub. CoRR abs/2209.11453 (2022) - 2021
- [c6]Luigi Quaranta, Fabio Calefato, Filippo Lanubile:
A Taxonomy of Tools for Reproducible Machine Learning Experiments. DP@AI*IA 2021: 65-76 - [c5]Filippo Lanubile, Fabio Calefato, Luigi Quaranta, Maddalena Amoruso, Fabio Fumarola, Michele Filannino:
Towards Productizing AI/ML Models: An Industry Perspective from Data Scientists. WAIN@ICSE 2021: 129-132 - [c4]Luigi Quaranta, Fabio Calefato, Filippo Lanubile:
KGTorrent: A Dataset of Python Jupyter Notebooks from Kaggle. MSR 2021: 550-554 - [d2]Luigi Quaranta, Fabio Calefato, Filippo Lanubile:
collab-uniba/KGTorrent: First release (v. 1.0.0) of KGTorrent. Zenodo, 2021 - [d1]Luigi Quaranta, Fabio Calefato, Filippo Lanubile:
KGTorrent: A Dataset of Python Jupyter Notebooks from Kaggle. Zenodo, 2021 - [i4]Filippo Lanubile, Fabio Calefato, Luigi Quaranta, Maddalena Amoruso, Fabio Fumarola, Michele Filannino:
Towards Productizing AI/ML Models: An Industry Perspective from Data Scientists. CoRR abs/2103.10548 (2021) - [i3]Luigi Quaranta, Fabio Calefato, Filippo Lanubile:
KGTorrent: A Dataset of Python Jupyter Notebooks from Kaggle. CoRR abs/2103.10558 (2021)
2010 – 2019
- 2019
- [c3]Daniela Girardi, Filippo Lanubile, Nicole Novielli, Luigi Quaranta, Alexander Serebrenik:
Towards recognizing the emotions of developers using biometrics: the design of a field study. SEmotion@ICSE 2019: 13-16 - [c2]Fabio Calefato, Filippo Lanubile, Nicole Novielli, Luigi Quaranta:
EMTk: the emotion mining toolkit. SEmotion@ICSE 2019: 34-37 - [c1]Davide Fucci, Daniela Girardi, Nicole Novielli, Luigi Quaranta, Filippo Lanubile:
A replication study on code comprehension and expertise using lightweight biometric sensors. ICPC 2019: 311-322 - [i2]Davide Fucci, Daniela Girardi, Nicole Novielli, Luigi Quaranta, Filippo Lanubile:
A Replication Study on Code Comprehension and Expertise using Lightweight Biometric Sensors. CoRR abs/1903.03426 (2019) - [i1]Fabio Calefato, Filippo Lanubile, Nicole Novielli, Luigi Quaranta:
EMTk - The Emotion Mining Toolkit. CoRR abs/1903.09525 (2019)
Coauthor Index
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last updated on 2025-02-05 20:32 CET by the dblp team
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