I am not sure how is that going to scale for bigger programs. In simple cases, I can model behavior of the program in my head, in more complicated cases, without aggregation, there will be too much data to make sense of it anyway. I think the aggregation of the statistical data is the crucial thing here.

Anyway, good that someone is playing with this.

The point of the abstractions presented in the essay is that they are under the programmer's control (write code to debug code isn't that new), so they only see as much as they want to. This is in contrast to automatic methods that show everything and are too noisy.

Scaling will perhaps come with time, by which I mean managed time. It is a nice goal for next gen languages.

What do you think of the work that Philip Guo [0] is doing? Specifically the youtube video.

[0] https://news.ycombinator.com/item?id=8002839

Philip has more of an educational focus, so more is shown to help then understand what is going on (also, see Bret Victor's Learnable Programming Essay). The work presented in this essay deals with enhancing the debugging experience for programmers who are already trained up.