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Every now and then you'll find automated code coverage tooling getting in the way of you landing changes when you're pretty certain you've written tests for the code in question.
Let's take some time to dig into how code coverage works and then use that new knowledge to find ways to unblock ourselves.
In the software and computer industry, we have a tendency to assume that the world, its inhabitants, and most of all ourselves are deterministically rational beings. At the risk of inciting the ire of the orange site, this is patently inaccurate and allows us to justify a diverse array of logical fallacies.
Advent of Code 2023 has just kicked off, and I'm going to try something a bit different this year, I'm going to try and share useful concepts and patterns that play a role in solving each day's puzzle.
Today, I'm going to talk about some how I go about optimizing algorithms for practical performance on data-intensive problems in Rust. I find that knowing how to optimize algorithms for practical performance is one of those skills that many overlook because it's rarely talked about, and yet it can have significant implications on performance for your applications.