if code is hyper specific

it isnt.

Nothing exists in a vacuum.

There is a break in a sewer line in town, so a crew goes out to fix it.

The crew’s only purpose is to fix sewer lines, but somehow, you’re still late for work.

I’ll give you a real world analogy. Have you ever tried to balance rocks? You can get two or three rocks balanced but that could put one of the lower balance points out of balance. It’s like that. That is the assumption that code has a singular dependence and it’s all linear is incorrect in most circumstances. Code often has many dependencies and many other parts depending on it.

Your example is for graphics drivers, which are unbelievably complex, not to mention have to be designed to:

A) work in tandem with a crazy amount of different architectures, kernels, hardware setups and dependencies.

B) must reliably manage the demands of millions of complex calculations, monitor throughput and heat dissipation, handle complex and varying game configurations and technologies, all while simultaneously working as seamlessly as possible for us users.

The breaking could be from any one of the above systems and more having any number is issues or changes.

Simplified example: Function #185 requires specific feedback from system hardware output #768, but that output was updated by the another development team for another system driver to give the word “true” or “false” instead of a number indicating the same. Now, if that function is not designed to handle the change gracefully beforehand, then there is a problem. Hour severe the problem is depends on what subsystem of the driver software is affected.

Monitoring system? Maybe not a biggie, the graphing app gets killed or freezes. The actual driver gets the issue? Could be catastrophic failure, where the system freezes or restarts, or corrupts the driver install itself and you need to reinstall previous version. Worse case scenario, it really fails and fries hardware, but that’s rare. Failsafes usually kick in on the hardware side to mitigate that kind of damage.

Now times this by a probable factor of thousands of functions and hundreds of dependencies and you get the idea.

I’m not great at explaining things sometimes, so forgive me if the above doesn’t answer your question.

Imagine you’re building a house, and you notice that a wall is not quite in the right spot, so you need to move it over a few inches. Sounds like a small change (just a few inches), but it’s easy to imagine that the construction might affect other parts of the house, or that you might see issues later because of the wall being moved over.

Not a perfect analogy, but a software change can sometimes seem small (just a few inches), but actually require a lot of complication

Software is generally built up in layers from reusable pieces. If there’s an assumption or a change in a lower layer or in one of those reusable pieces, things that might seem unrelated can break if they share one of those pieces

Exactly. Designs are tangled, causing unexpected explosions in the next building. And when I invite programmers to create less-tangled designs, they scoff and call it “overengineering”.

And then another explosion in the next building.

🤷

(This isn’t the only cause/effect path leading to unexpected defects, but it’s a very common one.)

The different parts of software are (maybe) meant to be independent, encapsulated from each other, but in practice it’s hard to actually do that. So if you change the behavior of one part, the other parts that depend on it directly or indirectly often need to be changed as well, and certain types of issues are easy to miss, especially when you aren’t targeting a homogenous hardware platform like a game console.

I’m system design there’s almost always a tension between efficiency and adaptability / robustness.

You can write software where code is very isolated and not shared and it makes it really easy to update it quickly and ship it and know that it will run properly, but the cost is usually that there’s more boundaries in the software that create little slowdowns and cause things to have to repeat and it adds up to a slower or more bloated program.

You also have instances where you write your software and it has a bug, and you later go to fix that bug, and you don’t realize that a whole bunch of other software relied on that bug being there.

For instance the Node.js team recently released a security patch that caused them to emit a proper .closing event. Unfortunately an old version of a very popular fetching library breaks when it receives that .closing event because is thought it was supposed to close. It’s fixable but it would be very hard for the node team to know that they would break another piece of software that was listening to undocumented events.

You want some examples?

Example 1:

lets say you have a variable named x

x is used in a lot of functions and classes, it’s a very important integer.

A function requires x to be a floating point number, though. Instead of creating a new variable, the developer makes x equal a float, which breaks all the functions expecting an int.

Example 2:

Developer uses a library A to build his own library B which in turn is relied upon by others. When Dev has to change this library A to a new library C due to deprecation or security vulnerability, they might not even notice any problems until later when it turns out the people using his library were already using a different library with the same name as library C, causing an industry standard software to fail to build for several days and millions of dollars lost.

Example 3:

Your code actually had all the bugs the entire time but it broke before it could even reach them, so you only saw one error until you fixed it.

Good programmers try to follow the DRY principle: Don’t Repeat Yourself. A lot of times you need to do the same actions over and over in different parts of a program, so you write a function to do that, then just repeatedly call the function. If you have to modify a function to fix a bug in one place it might change the input/output somewhere else unexpected.

It’s the tablecloth effect:

You sit at a beautiful and elegant table, with plates and glasses and food and all. You pull at the tablecloth on your side so maybe the glasses on your side fall over. But maybe also the glasses on the other side fall over.

They were connected somehow. Maybe you knew the connection, maybe not.

There are many routes, but in your example, for instance, Microsoft updates don’t just have 1 fix for 1 issue but multiple fixes and new features, ideally Microsoft would have tests that cover all usages that they run before deployment, but they don’t (it’s a lot of code) but also it has to run on any combination of systems.

Then you throw vibe coding into the mix…

Another thing is if you had 2 similarly named variables and use the wrong one it might work one way but not another, someone is tasked to fix it and they see it using the wrong variable and correct it… great, but maybe other stuff was made since that now breaks because that variable is correct now.

And any issue relating to timing issues can just appear and disappear based on environmental conditions, a true nightmare to resolve well.