Incorporating machine learning (ML) components into software products raises new
software-engineering challenges and exacerbates existing ones. Many researchers have …

AI-powered code assistants, such as Copilot, are quickly becoming a ubiquitous component
of contemporary coding contexts. Among these environments, computational notebooks …

LLM-powered tools like ChatGPT Data Analysis, have the potential to help users tackle the
challenging task of data analysis programming, which requires expertise in data processing …

Profiling data by plotting distributions and analyzing summary statistics is a critical step
throughout data analysis. Currently, this process is manual and tedious since analysts must …

Jupyter notebooks enable developers to interleave code snippets with rich-text and in-line
visualizations. Data scientists use Jupyter notebook as the de-facto standard for creating …

Jupyter notebooks have become indispensable tools for data analysis and processing in
various domains. However, despite their widespread use, there is a notable research gap in …

Background. Due to the widespread adoption of Artificial Intelligence (AI) and Machine
Learning (ML) for building software applications, companies are struggling to recruit …

Jupyter notebooks have emerged as the predominant tool for data scientists to develop and
share machine learning solutions, primarily using Python as the programming language …

Developers rely on software ecosystems such as Maven to manage and reuse external
libraries (ie, dependencies). Due to the complexity of the used dependencies, developers …

Abstract Developing reusable Big Data Systems (BDSs) implies dealing with modeling
variety as reusable assets. Conceptually speaking, these assets might be similar to reusable …