[1/*] If we take the chance that a tool (compiler, linker, batch, whatever) remains working for a particular codebase after one year as a given probability p, then the chance that build remains working after x years is p^xn, where n is the number of tools used in the build.
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While the real model is much more complicated than what I graphed, I'm pretty sure that the underlying concept of exponential probability is inescapable, honestly, so whatever the real graph does look like, it basically does something like this.
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I'd say sub-exponential, but also super-linear. There's a related problem with large software (e.g. android). Where every team/component targets a certain (low) bug rate, but as the number of teams/components increase the probability that a user has a bug-free experience drops.
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I write as many hobby and commercial applications, as i can, in Pascal with as near to zero dependencies as possible. After 12 years, only a few projects failed to compile due to being incompatible with the new tooling. It's why I don't NodeJS & npm. I think your math is spot on.
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node.js is insane this way - I don't think I've ever updated Electron and had it work. You always have to modify your code for one reason or another, no matter how simple it may be!
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