Increasingly larger number of software systems today are including data science
components for descriptive, predictive, and prescriptive analytics. The collection of data …

Artificial intelligence systems, such as Sentiment Analysis (SA) systems, typically learn from
large amounts of data that may reflect human bias. Consequently, such systems may exhibit …

Computational notebooks have become the tool of choice for many data scientists and
practitioners for performing analyses and disseminating results. Despite their increasing …

Background Jupyter notebooks facilitate the bundling of executable code with its
documentation and output in one interactive environment, and they represent a popular …

In recent years, Jupyter notebooks have grown in popularity in several domains of software
engineering, such as data science, machine learning, and computer science education …

Managing cross-project dependencies is tricky in modern software development. A primary
way to manage dependencies is using dependency configuration files, which brings …

Machine learning (ML) components are increasingly incorporated into software products, yet
developers face challenges in transitioning from ML prototypes to products. Academic …

As one of the representative software ecosystems, PyPI, together with the Python package
management tool pip, greatly facilitates Python developers to automatically manage the …

Executing code is essential for various program analysis tasks, eg, to detect bugs that
manifest through exceptions or to obtain execution traces for further dynamic analysis. How …

Despite the ubiquity of data science, we are far from rigorously understanding how coding in
data science is performed. Even though the scientific literature has hinted at the iterative and …