I'd add one more tip that I think far more software developers should do: Add unit-level fuzz testing throughout your projects. Fuzzy bois are like assertions on steroids.

With large projects you often get modules which have an API boundary, complex internals and clear rules for what correct / incorrect look like. For example, data structures or some complex algorithms. (A-star, or whatever).

Every time I have a system like this, I'm now in the habit of writing 3 pieces of code:

1. A function that checks the internal invariants are true. Eg, in a Vec, the allocated length should be >= the current length. In a sorted tree, if you iterate through the items, they're sorted. And children are always >= the internal nodes (or whatever the rules are for your tree). During development, I wrap my state mutators in check() calls. This means I know instantly if one of my mutating functions has broken something. (This is a godsend for debugging.)

2. A function which randomly exercises the code, in a loop. Eg, if you're writing a hash table, write a function which creates a hash table and randomly inserts and deletes items in a loop for awhile. If you've implemented a search algorithm, generate random data and run searches on it. Most complex algorithms and data structures have simple ways to tell if the return value of a query is correct. So check everything. For example, a sorted tree should contain the same items in the same order as a sorted list. Its just faster. So if you're writing a sorted tree, have your randomizer also maintain a sorted list and then periodically check that the sorted list contains the same items in the same order as your tree. If you're writing A-star, check that an inefficient flood fill search returns the same result. Your randomizer should always be explicitly seeded so when it finds problems you can easily and deterministically reproduce them.

3. A test which calls the randomizer over and over again, and checks all the invariants are correct. When this can run overnight with optimizations enabled, your code is probably ok. There's a bunch of delicate performance balances to strike here - its easy to spend too much CPU time checking your invariants. If you do that, you won't find rare bugs because your test won't run enough times. I often end up with something like this:

    loop (ideally on all cores) {
        generate random seed
        initialize a new Foo
        for i in 0..100 {
            randomly make foo more complicated
            (at first check invariants here)
        }
        (then later move invariants here)
    }

Every piece of a large program should be tested like this. And if you can, test your whole program like this too. (Doable for most libraries, databases, compilers, etc. This is much harder for graphics engines or UI code.)

I've been doing this for years and I can't remember a single time I set something like this up and didn't find bugs. I'm constantly humbled by how effective fuzzy bois are.

This sounds complex, but code like this will usually be much smaller and easier to maintain than a thorough unit testing suite.

Here's an example from a rope (complex string) library I maintain. The library lets you insert or delete characters in a string at arbitrary locations. The randomizer loop is here[1]. I make Rope and a String, then in a loop make random changes and then call check() to make sure the contents match and all the internal invariants hold.

[1] https://github.com/josephg/jumprope-rs/blob/ae2a3f3c2bc7fc1f...

When I first ran this test, it found a handful of bugs in my code. I also ran this same code on a few rust rope libraries in cargo. About half of them fail this test.