Phone: +39 080 544 3261
Fax: +39 080 544 2031
Luigi Quaranta is a researcher at the University of Bari, Italy. His main research interest is Software Engineering for Artificial Intelligence. Currently, he is investigating the compelling benefits and common pitfalls of computational notebooks, with a focus on the Jupyter ecosystem; his goal is to improve the integration of computational notebooks in AI-enabled system building workflows, promoting the adoption of Software Engineering best practices.
- Software Engineering
- Artificial Intelligence
- Mining Software Repositories
- Collaboration in Software Development
- Social Software Engineering
Education and Background
Ph.D. in Computer Science (November 2022) – University of Bari, Italy
(Ph.D. Thesis awarded “cum Laude”)
Master’s degree in Computer Science (July 2019) – University of Bari, Italy
(full marks and honors)
Bachelor’s degree in Computer Science (October 2016) – University of Bari, Italy
(full marks and honors)
Organizing Committee Member
- CHASE’22 – Web Chair
- ESEM’21 – Web Chair (Distinguished Service Award)
- SSBSE’21 – Web Chair
- SSBSE’20 – Web Chair
Program Committee Member
- IEEE Transactions on Software Engineering (TSE), IEEE Computer Society
- Empirical Software Engineering (EMSE), Springer
- Journal of Systems and Software (JSS), Elsevier
- IEEE Software, IEEE Computer Society
- F. Lanubile, S. Martínez-Fernández, L. Quaranta, “Teaching MLOps in Higher Education through Project-Based Learning,” accepted for publication in the Proceedings of the 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET’23).
- F. Calefato, F. Lanubile, L. Quaranta, “A Preliminary Study of MLOps Practices in GitHub,” Challenges In Deploying And Monitoring Machine Learning Systems, NeurIPS Workshop.
- F. Calefato, F. Lanubile, L. Quaranta, “A Preliminary Investigation of MLOps practices in GitHub,” Proc. of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM ’22), doi: 10.1145/3544902.3546636. [arXiv]
- L. Quaranta, “Assessing the Quality of Computational Notebooks for a Frictionless Transition from Exploration to Production,” Proc. of 2022 IEEE/ACM 44th International Conference on Software Engineering Companion (ICSE’22 Companion), May 21-29, 2022, Pittsburgh, PA, USA, doi: 10.1145/3510454.3517055. [arXiv]
- L. Quaranta, F. Calefato and F. Lanubile, “Pynblint: a Static Analyzer for Python Jupyter Notebooks,” Proc. of 2022 IEEE/ACM 1st Conference on AI Engineering – Software Engineering for AI (CAIN’22), May 16-24, 2022, Pittsburgh, PA, USA, doi: 10.1145/3522664.3528612. [arXiv]
- L. Quaranta, F. Calefato and F. Lanubile, “Eliciting Best Practices for Collaboration with Computational Notebooks,” Proceedings of the ACM on Human-Computer Interaction, Volume 6, Issue CSCW1, April 2022, Article No.: 87, pp 1–41, doi: 10.1145/3512934. [arXiv]
- M. Serra, A. Presicci, L. Quaranta, et al., “Assessing clinical features of adolescents suffering from depression who engage in non-suicidal self-injury,” Children, Vol. 9, n. 2, 2022, p. 201, doi: 10.3390/children9020201.
- M. Serra, A. Presicci, L. Quaranta et al., “Depressive risk among Italian socioeconomically disadvantaged children and adolescents during COVID-19 pandemic: a cross-sectional online survey,” Ital J Pediatr 48, 68 (2022), doi: 10.1186/s13052-022-01266-x.
- M. Serra, A. Presicci, L. Quaranta, M. Achille, E. Caputo, S. Medicamento, F. Margari, F. Croce, L. Margari. “Associations of High-Sensitivity C-Reactive Protein and Interleukin-6 with Depression in a Sample of Italian Adolescents During COVID-19 Pandemic,” Neuropsychiatr Dis Treat. 2022;18:1287-1297, doi: 10.2147/NDT.S362536.
- L. Quaranta, F. Calefato and F. Lanubile, “A Taxonomy of Tools for Reproducible Machine Learning Experiments,” Proceedings of the AIxIA 2021 Discussion Papers Workshop (AIxIA DP 2021), 2021, pp. 65-76, online: CEUR-WS.org/Vol-3078/paper-81.pdf
- L. Quaranta, F. Calefato and F. Lanubile, “KGTorrent: A Dataset of Python Jupyter Notebooks from Kaggle,” 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR), 2021, pp. 550-554, doi: 10.1109/MSR52588.2021.00072. [arXiv]
- F. Lanubile, F. Calefato, L. Quaranta, M. Amoruso, F. Fumarola and M. Filannino, “Towards Productizing AI/ML Models: An Industry Perspective from Data Scientists,” 2021 IEEE/ACM 1st Workshop on AI Engineering – Software Engineering for AI (WAIN), 2021, pp. 129-132, doi: 10.1109/WAIN52551.2021.00027. [arXiv]
- D. Fucci, D. Girardi, N. Novielli, L. Quaranta and F. Lanubile, “A Replication Study on Code Comprehension and Expertise using Lightweight Biometric Sensors,” 2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC), 2019, pp. 311-322, doi: 10.1109/ICPC.2019.00050.
- F. Calefato, F. Lanubile, N. Novielli and L. Quaranta, “EMTk – The Emotion Mining Toolkit,” 2019 IEEE/ACM 4th International Workshop on Emotion Awareness in Software Engineering (SEmotion), 2019, pp. 34-37, doi: 10.1109/SEmotion.2019.00014.
- D. Girardi, F. Lanubile, N. Novielli, L. Quaranta and A. Serebrenik, “Towards Recognizing the Emotions of Developers Using Biometrics: The Design of a Field Study,” 2019 IEEE/ACM 4th International Workshop on Emotion Awareness in Software Engineering (SEmotion), 2019, pp. 13-16, doi: 10.1109/SEmotion.2019.00010.