no code implementations • 20 Jul 2023 • Fabio Calefato, Luigi Quaranta, Filippo Lanubile, Marcos Kalinowski
In this scenario, AutoML is soaring as a promising solution to fill the AI/ML skills gap since it promises to automate the building of end-to-end AI/ML pipelines that would normally be engineered by specialized team members.
no code implementations • 2 Feb 2023 • Filippo Lanubile, Silverio Martínez-Fernández, Luigi Quaranta
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.
no code implementations • 23 Sep 2022 • Fabio Calefato, Filippo Lanubile, Luigi Quaranta
Our preliminary results suggest that the adoption of MLOps workflows in open-source GitHub projects is currently rather limited.
no code implementations • 24 May 2022 • Luigi Quaranta, Fabio Calefato, Filippo Lanubile
Jupyter Notebook is the tool of choice of many data scientists in the early stages of ML workflows.
1 code implementation • 15 Feb 2022 • Luigi Quaranta, Fabio Calefato, Filippo Lanubile
In this paper, we fill this gap by eliciting a catalog of best practices for collaborative data science with computational notebooks.
1 code implementation • 11 Oct 2021 • Fabio Calefato, Filippo Lanubile
Assessing the personality of software engineers may help to match individual traits with the characteristics of development activities such as code review and testing, as well as support managers in team composition.
no code implementations • 18 Mar 2021 • Filippo Lanubile, Fabio Calefato, Luigi Quaranta, Maddalena Amoruso, Fabio Fumarola, Michele Filannino
Starting from the need for making AI experiments reproducible, the main themes that emerged are related to the use of the Jupyter Notebook as the primary prototyping tool, and the lack of support for software engineering best practices as well as data science specific functionalities.
no code implementations • 17 Mar 2018 • Nicole Novielli, Daniela Girardi, Filippo Lanubile
A recent research trend has emerged to identify developers' emotions, by applying sentiment analysis to the content of communication traces left in collaborative development environments.
Software Engineering
1 code implementation • 12 Oct 2017 • Fabio Calefato, Filippo Lanubile, Nicole Novielli
We quantitatively analyze a set of over 87K questions from the official Stack Overflow dump to assess the impact of actionable factors on the success of technical requests.
Computers and Society
1 code implementation • 9 Sep 2017 • Fabio Calefato, Filippo Lanubile, Federico Maiorano, Nicole Novielli
The role of sentiment analysis is increasingly emerging to study software developers' emotions by mining crowd-generated content within social software engineering tools.
2 code implementations • 13 Aug 2017 • Fabio Calefato, Filippo Lanubile, Nicole Novielli
We provide empirical evidence of the performance of EmoTxt.
no code implementations • 27 Mar 2017 • Mika V. Mäntylä, Nicole Novielli, Filippo Lanubile, Maëlick Claes, Miikka Kuutila
Emotional arousal increases activation and performance but may also lead to burnout in software development.