It reminds me an approach to data science and ML. It's way more common and essential for ML teams to log their attempts (experiments). There is a whole set of tools for this - experiments trackers. Primarily to even being able to compare and pick the best direction, but also to ensure reproducibility (in some areas it might be required). ML/DS always seemed to me closer to science in its nature vs software engineerin…
Right, i have felt this way too (even though i am just a noob at ML/DS). For me it is the use of Statistics/Probability/Mathematics in driving understanding/intuition about the problem.