Cross-posted from the Helical project blog.
In my previous post, I asserted:
...learning a new formal language can itself contribute to the difficulty of encoding an experiment.
This statement was based on assumptions, intuitions, and folk wisdom. I started digging into the DSL usability research to see if I could find explicit support for this statement. This blog post is about what I found.
Cross-posted from the Helical project blog.
One of the broader goals of the Helical project is to make writing, maintaining, and debugging experiments easier and safer for the end-user through a novel domain-specific language. However, learning a new formal language can itself contribute to the difficulty of encoding an experiment. Therefore, we are intersted in mitigating the effects of language learning/novelty. To this end, a Northeastern coop student (Kevin G. Yang) investigated the suitability of using Jupyter notebooks as an execution environment for experiments last year.
Cross-posted from the Helical project blog.
I want to extend a belated welcome to Zixuan (Jason) Yu, a Northeastern University undergraduate student who is working with me on a research coop through December 2025. Jason's project focuses on identifying elements of the Mastodon code base where we might either want to intervene (in order to answer a research question) or where there might be associated privacy considerations.