This is a neat thought experiment, but it seems to me it's looking at a different part of the curve than software complexity. The "phase transition" the author

This is a neat thought experiment, but it seems to me it's looking at a different part of the curve than software complexity. The "phase transition" the author discusses occurs between 0 and 1 threads/button. In a software project, you wouldn't bring in a new component unless it served some use to the existing pieces, so software projects start at 1 "thread/button," and unless you've got orphaned code, the "cluster size" is always 100%. Software complexity strikes me as a graph-coverage problem: given a graph of N vertices and M paths (i.e. software components and dependencies), how many vertices and paths do we need to traverse (i.e. understand) in order to make a change to component X? How does that parameter scale with different forms of graph -- linear, n-ary tree, DAG, cyclic (yikes!)? Or is there a homomorphism between the two problems?