Laws of Software Engineering

Fabian Parzer — Fri, 07 Aug 2026 00:00:00 GMT

I love this website.

My favourite law from the site is Gilb's law: "Anything you need to quantify can be measured in some way better than not measuring it." There are many similar "laws" related to quantitative methods, for example, "There is no alternative to forecasting". These can all be seen as instances of the "perfect is the enemy of good" principle, and they refute a common objection to quantitative modelling: that the use case is too complex and the data are too poor. But I do not have to model reality perfectl, I only have to be better than the current situation, which is often "no model".

Personal Programming Guidelines

Fabian Parzer — Sat, 02 May 2026 00:00:00 GMT

“The secret of good writing is to strip every sentence to its cleanest components.”

— William Zinsser, On Writing Well

Programmers are known to often obsess over what is and is not "good code." Google is famous for its Readability process, which is seen as a role model but has some critics even inside the company (see also Software Engineering at Google and Readability: Google's Temple to Engineering Excellence). I have seen disagreements on best practices lead to lengthy, exhausting discussions in meetings or during code review, delaying important work without leading anywhere. I have also seen some colleagues, for these reasons, develop the reactionary view that insisting on clean code and enforcing a lot of software patterns are impractical for real-world projects and something that most enterprises outside Big Tech cannot afford. I recognise that an over-obsession with coding standards can be detrimental to project success, and that the requirements for code quality and maintainability should take the expected scope and lifetime of the code (one-off notebook vs. PoC vs. core infrastructure) into account. But I'm not a big fan of the "we'll fix it later" mentality.

Mathematical Dev (Posts about programming)

Laws of Software Engineering

Personal Programming Guidelines