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Found 13 papers, 5 papers with code
Assessing the Use of AutoML for Data-Driven Software Engineering
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.
Teaching MLOps in Higher Education through Project-Based Learning
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.
A Preliminary Investigation of MLOps Practices in GitHub
Our preliminary results suggest that the adoption of MLOps workflows in open-source GitHub projects is currently rather limited.
Pynblint: a Static Analyzer for Python Jupyter Notebooks
Jupyter Notebook is the tool of choice of many data scientists in the early stages of ML workflows.
Eliciting Best Practices for Collaboration with Computational Notebooks
In this paper, we fill this gap by eliciting a catalog of best practices for collaborative data science with computational notebooks.
Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?
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.
Towards Productizing AI/ML Models: An Industry Perspective from Data Scientists
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.
A Benchmark Study on Sentiment Analysis for Software Engineering Research
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
How to Ask for Technical Help? Evidence-based Guidelines for Writing Questions on Stack Overflow
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
Sentiment Polarity Detection for Software Development
The role of sentiment analysis is increasingly emerging to study software developers' emotions by mining crowd-generated content within social software engineering tools.
EmoTxt: A Toolkit for Emotion Recognition from Text
We provide empirical evidence of the performance of EmoTxt.
Bootstrapping a Lexicon for Emotional Arousal in Software Engineering
Emotional arousal increases activation and performance but may also lead to burnout in software development.
