Machine Learning systems are, by now, an essential part of the software landscape. From
the development perspective this means a paradigmatic shift, which should be reflected in …

Computational notebooks like the Jupyter programming environment have been popular,
particularly for developing data-driven applications. One of its main benefits is that it easily …

AI-powered programming assistants are increasingly gaining popularity, with GitHub Copilot
alone used by over a million developers worldwide. These tools are far from perfect …

Software development of modern, data-driven applications still relies on tools that use
interaction paradigms that have remained mostly unchanged for decades. While rich forms …

Software development of modern, data-driven applications still relies on tools that use
interaction paradigms that have remained mostly unchanged for decades. While rich forms …

Program synthesizers promise to save time for a programmer by writing parts of their code
for them. Presently, these tools work best when a programmer knows exactly what they want …

The intelligent programming assistant, directly integrated into the programmer's workflow,
has been a long time dream of programmers and researchers alike. Before the advent of …

Debugging GUI applications is challenging because of the difficult-to-debug state changes
caused by asynchronous event handling and user interactions. Live programming, a …

Common debugging techniques are execution-first, requiring programmers to probe into
execution via print logging or breakpoints to inspect intermediate program states. To …

Interactive GUI applications are crucial to human-computer interaction. Despite existing tools
and frameworks that aim to reduce such complexity, understanding and debugging these …