Coding on Copilot: Data suggests downward pressure on code quality

Maybe lazy is the wrong term, but less attentive apparently. There was the Ironies of Automation paper about this: https://ckrybus.com/static/papers/Bainbridge_1983_Automatica...

Code reuse is not a virtue it's a tool. WET is a good way to write code anyway. And at the end of the day, programmers who don't know what they are really trying to accomplish are never going to write good code. AI will just help them write different bad code faster.

First consider following: The problem with current LLMS they generate code which in addition to any obvious messiness have the tendency to look plausible even if wrong. I.e. the kind of bugs which are overlooked in reviews the most are also the kinds of bugs LLMS are most likely to have "accidentally learned as correct behaviour".

Then: Outside of some extra complex or absurdly simple case it is very often harder to write tests which truly and fully test your code then it is to write the code correct.

In my experience often correct code is a product of carefully written test (which still are in reality imperfect), static code analysis (can be the type system, or external tools) applied to carefully written code and a proper code review.

So if you bring both of it together you have:

- AI supported code which is likely to contain bugs which are really easy to overlook in reviews

- AI supported test code which is has the same issue, i.e. they have gaps which are really likely to overlook by reviewers.

- more code due to less reuse and it also sometimes being easier to generate instead of use a library leading to more code review needing to be done and in turn more time pressure and less quality review

so put together: more bugs which are hard to find with test which are more likely subtle pass even with bugs and less time for proper reviews

So does it pass the test? Yes, but it was AI written too so can it be trusted?

IMHO there is some form of crisis of "value production speed" for various programming things.

And AI gives you fast code production speed so it seems to be a fix.

But due to less structured code and code reuse it needs much more code for the same value produced. And it loves to produce the kind of bugs which are easy to pass reviews and tests (because this are the kind of bugs it most likely has as "correct" code in it's training data). Which means less time per-value for human code reviews and in turn lower quality results on many levels.

I originally hoped that with CS/SoftwareDev getting older and less explosive grows and Universities more realizing the (sometimes huge) gap between CS and SoftwareDev the industry would get a chance of consolidating tech and dev processes to fix that issue. But with the rise of LLMs this hope might be further away then it ever had been in the last 20 year.