Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Quaranta, Luigi"'
Context. Advancements in Machine Learning (ML) are revolutionizing every application domain, driving unprecedented transformations and fostering innovation. However, despite these advances, several organizations are experiencing friction in the adopt
Externí odkaz:
http://arxiv.org/abs/2401.11366
Publikováno v:
Proc. of 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM'23), Oct. 2023
Background. Due to the widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) for building software applications, companies are struggling to recruit employees with a deep understanding of such technologies. In this scenario, A
Externí odkaz:
http://arxiv.org/abs/2307.10774
Publikováno v:
Information and Software Technology, vol. 176, 2024
Context. GitHub has introduced a new gamification element through personal achievements, whereby badges are unlocked and displayed on developers' personal profile pages in recognition of their development activities. Objective. In this paper, we pres
Externí odkaz:
http://arxiv.org/abs/2303.14702
Publikováno v:
2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)
Building and maintaining production-grade ML-enabled components is a complex endeavor that goes beyond the current approach of academic education, focused on the optimization of ML model performance in the lab. In this paper, we present a project-bas
Externí odkaz:
http://arxiv.org/abs/2302.01048
Publikováno v:
Proceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2022), September 2022, Pages 283-288
Background. The rapid and growing popularity of machine learning (ML) applications has led to an increasing interest in MLOps, that is, the practice of continuous integration and deployment (CI/CD) of ML-enabled systems. Aims. Since changes may affec
Externí odkaz:
http://arxiv.org/abs/2209.11453
Autor:
Quaranta, Luigi
Publikováno v:
Proc. of 2022 IEEE/ACM 44th International Conference on Software Engineering Companion (ICSE 22 Companion), May 21-29, 2022, Pittsburgh, PA, USA
The massive trend of integrating data-driven AI capabilities into traditional software systems is rising new intriguing challenges. One of such challenges is achieving a smooth transition from the explorative phase of Machine Learning projects - in w
Externí odkaz:
http://arxiv.org/abs/2205.11941
Publikováno v:
Proc. of 2022 IEEE/ACM 1st Conference on AI Engineering - Software Engineering for AI (CAIN), May 16-24, 2022, Pittsburgh, PA, USA
Jupyter Notebook is the tool of choice of many data scientists in the early stages of ML workflows. The notebook format, however, has been criticized for inducing bad programming practices; indeed, researchers have already shown that open-source repo
Externí odkaz:
http://arxiv.org/abs/2205.11934
Publikováno v:
Proc. ACM Hum.-Comput. Interact., Vol. 6, No. CSCW1, Article 87, April 2022
Despite the widespread adoption of computational notebooks, little is known about best practices for their usage in collaborative contexts. In this paper, we fill this gap by eliciting a catalog of best practices for collaborative data science with c
Externí odkaz:
http://arxiv.org/abs/2202.07233
Publikováno v:
In Information and Software Technology December 2024 176
Publikováno v:
In The Journal of Systems & Software April 2024 210