h2c VS stats

> do not believe in the possibility of a "General Theory of Productivity." I'm highly skeptical of attempts to quantify the precise relationship between error discovery stage and cost in a way that is generalizable, although I think it might be possible given a large group of engineers using a highly homogenous process, tools, and accounting. Google is pretty close to this (common dev infrastructure across tens of thousands of engineers), and even across Google this kind of generalization would be extremely difficult.

I don't think you are incorrect, but I think a lot of the aspirants behind ESE just want to have a better sense of what works and what doesn't; I'd even welcome negative results! The current state of things is to read 100 opinionated people and their blog posts. And given enough time, you'll encounter someone who swears that after drinking their morning coffee and jumping on one foot for 1 min, they enter a VRChat standup with their team and hit max flow. There's just so little knowledge right now about what works and what doesn't that I'd welcome more clarity, especially negative results.

> As a result, academic research into productivity can be difficult to generalize

I think defects are what we should measure for, not productivity because of the subjectivity of measuring productivity. But even measuring defects is complicated. The best way I see to measure defects is to ask a Team Under Test to document bugs that they encounter along with resolution times, but this is not only expensive, but something I doubt most corporations will be willing to share outside of their walls. Perhaps open source projects can try to store this data, like curl's stats [1].

[1]: https://github.com/curl/stats