Using FizzBuzz as your proxy for "unreviewed code" is extremely misleading. It has practically no complexity, it's completely self-contained and easy to verify. In any codebase of even modest complexity, the challenge shifts from "does this produce the correct outputs" to "is this going to let me grow the way I need it to in the future" and thornier questions like "does this have the performance characteristics that I need".
> is this going to let me grow the way I need it to in the future
This doesn’t matter in the age of AI - when you get a new requirement just tell the AI to fulfill it and the old requirements (perhaps backed by a decent test suite?) and let it figure out the details, up to and including totally trashing the old implementation and creating an entirely new one from scratch that matches all the requirements.
For performance, give the AI a benchmark and let it figure it out as well. You can create teams of agents each coming up with an implementation and killing the ones that don’t make the cut.
Or so goes the gospel in the age of AI. I’m being totally sarcastic, I don’t believe in AI coding
> including totally trashing the old implementation and creating an entirely new one from scratch that matches all the requirements
Let me guess, you've never worked in a real production environment?
When your software supports 8, 9, 10 or more zeroes of revenue, "trash the old and create new" are just about the scariest words you can say. There's people relying on this code that you've never even heard of.
I work on a product that meets your criteria. We can't fix a class of defects because once we ship, customers will depend upon that behavior and changing is very expensive and takes years to deprecate and age out. So we are stuck with what we ship and need to be very careful about what we release.
it isn't gospel, it's perspective. if you care about the code, it's obviously bonkers. if you care about the product... code doesn't matter - it's just a means to an end. there's an intersection of both views in places where code actually is the product - the foundational building blocks of today's computing software infrastructure like kernels, low level libraries, cryptography, etc. - but your typical 'uber for cat pictures' saas business cares about none of this.
This is a naïve approach, not just because it uses FizzBuzz, but because it ignores the fundamental complexity of software as a system of abstractions. Testing often involves understanding these abstractions and testing for/against them.
For those of us with decades of experience and who use coding agents for hours per-day, we learned that even with extended context engineering these models are not magically covering the testing space more than 50%.
If you asked your coding agent to develop a memory allocator, it would not also 'automatically verify' the memory allocator against all failure modes. It is your responsibility as an engineer to have long-term learning and regular contact with the world to inform the testing approach.
While I understand why people want to skip code reviews, I think it is an absolute mistake at this point in time. I think AI coding assistants are great, but I've seen them fail or go down the wrong path enough times (even with things like spec driven development) where I don't think it's reasonable to not review code. Everything from development paths in production code, improper implementations, security risks: all of those are just as likely to happen with an AI as a Human, and any team that let's humans push to production without a review would absolutely be ridiculed for it.
Again, I'm not opposed to AI coding. I know a lot of people are. I have multiple open source projects that were 100% created with AI assistants, and wrote a blog post about it you can see in my post history. I'm not anti-ai, but I do think that developers have some responsibility for the code they create with those tools.
I agree that it would be a mistake to use something like this in something where people depend upon specific behaviour of the software. The only way we will get to the point where we can do this is by building things that don't quite work and then start fixing the problems. Like AI models themselves, where they fail is on problems that they couldn't even begin to attempt a short time ago. That loses track of the fact that we are still developing this technology. Premature deployment will always be fighting against people seeking a first mover advantage. People need to stay aware of that without critisising the field itself.
There are a subset of things that it would be ok to do this right now. Instances where the cost of utter failure is relatively low. For visual results the benchmark is often 'does it look right?' rather than 'Is it strictly accurate?"
Who writes the tests? It can be ok to trust code that passes tests if you can trust the tests.
There are, however, other problems. I frequently see agents write code that's functionally correct but that they won't be able to evolve for long. That's also what happened with Anthropic's attempt to have agents write a C compiler. They had thousands of human-written tests, but at some point the agents couldn't get the software to converge. Fixing a bug created another.
I do think that GenAI will lead to a rise in mutation testing, property testing, and fuzzing. But it's worth people keeping in mind that there are reasons why these aren't already ubiquitous. Among other issues, they can be computationally expensive, especially mutation testing.
does the cost of writing good property tests scale better than the cost of code review as the codebase grows? seems like the bottleneck just moves from reviewing code to reviewing specs.
People treating this as a scaling problem are skipping the part where verification runs into undecidability fast.
Proving a small pure function is one thing, but once the code touches syscalls, a stateful network protocol, time, randomness, or messy I/O semantics, the work shifts from 'verify the program' to 'model the world well enough that the proof means anything,' and that is where the wheels come off.
I think generative AI works best as a code review assistant rather than something that should be relied entirely upon for code review. LLMs can provide helpful advice and help engineers think of something they might not have thought about when writing, but they cannot review code in-depth the way we can. There are plenty of blatantly obvious security issues (usually on code generated by an LLM in large patches) I've seen pass LLM and automated code review before.
LLM code review (with the right tools and setup) can encourage engineers to write more pragmatic code, help write more in-depth tests, and submit overall better code without having to involve someone else, but a language model can't replace an actual human code review for production systems. We also should not be thinking of code as "correct" or "incorrect" because that doesn't measure code quality and security. Additionally, code can be classified as correct through tests but still have major security issues not covered by a test or be something that utilizes too much CPU/memory in production.
Mutation is a test for the test suite. The question is whether a change to the program is detected by the tests. If it's not, the test suite lacks coverage.
That's a high standard for test suites, and requires heavy testing of the obvious.
But if you actually can specify what the program is supposed to do, this can work. It's appropriate where the task is hard to do but easy to specify. A file system or a database can be specified in terms of large arrays. Most of the complexity of a file system is in performance and reliability. What it's supposed to do from the API perspective isn't that complicated. The same can be said for garbage collectors, databases, and other complex systems that do something that's conceptually simple but hard to do right.
Probably not going to help with a web page user interface. If you had a spec for what it was supposed to do, you'd have the design.
Correct. Where did the engineering go? First it was in code files. Then it went to prompts, followed by context, and then agent harnesses. I think the engineering has gone into architecture and testing now.
We are simply shuffling cognitive and entropic complexity around and calling it intelligence. As you said, at the end of the day the engineer - like the pilot - is ultimately the responsible party at all stages of the journey.
I might be missing the point of the article, but from what I understand, the TL;DR is, "cover your code with tests", be it unit tests, functional tests, or mutants.
Each of these approaches is just fine and widely used, and none of them can be called "automated verification", which, if my understanding of the term is correct, is more about mathematical proof that the program works as expected.
The article mostly talks about automatic test generation.
This doesn’t matter in the age of AI - when you get a new requirement just tell the AI to fulfill it and the old requirements (perhaps backed by a decent test suite?) and let it figure out the details, up to and including totally trashing the old implementation and creating an entirely new one from scratch that matches all the requirements.
For performance, give the AI a benchmark and let it figure it out as well. You can create teams of agents each coming up with an implementation and killing the ones that don’t make the cut.
Or so goes the gospel in the age of AI. I’m being totally sarcastic, I don’t believe in AI coding
Let me guess, you've never worked in a real production environment?
When your software supports 8, 9, 10 or more zeroes of revenue, "trash the old and create new" are just about the scariest words you can say. There's people relying on this code that you've never even heard of.
Really good post about why AI is a poor fit in software environments where nobody even knows the full requirements: https://www.linkedin.com/pulse/production-telemetry-spec-sur...
For those of us with decades of experience and who use coding agents for hours per-day, we learned that even with extended context engineering these models are not magically covering the testing space more than 50%.
If you asked your coding agent to develop a memory allocator, it would not also 'automatically verify' the memory allocator against all failure modes. It is your responsibility as an engineer to have long-term learning and regular contact with the world to inform the testing approach.
Again, I'm not opposed to AI coding. I know a lot of people are. I have multiple open source projects that were 100% created with AI assistants, and wrote a blog post about it you can see in my post history. I'm not anti-ai, but I do think that developers have some responsibility for the code they create with those tools.
There are a subset of things that it would be ok to do this right now. Instances where the cost of utter failure is relatively low. For visual results the benchmark is often 'does it look right?' rather than 'Is it strictly accurate?"
Who writes the tests? It can be ok to trust code that passes tests if you can trust the tests.
There are, however, other problems. I frequently see agents write code that's functionally correct but that they won't be able to evolve for long. That's also what happened with Anthropic's attempt to have agents write a C compiler. They had thousands of human-written tests, but at some point the agents couldn't get the software to converge. Fixing a bug created another.
Proving a small pure function is one thing, but once the code touches syscalls, a stateful network protocol, time, randomness, or messy I/O semantics, the work shifts from 'verify the program' to 'model the world well enough that the proof means anything,' and that is where the wheels come off.
Just write your business requirements in a clear, unambiguous and exhaustive manner using a formal specification language.
Bam, no coding required.
LLM code review (with the right tools and setup) can encourage engineers to write more pragmatic code, help write more in-depth tests, and submit overall better code without having to involve someone else, but a language model can't replace an actual human code review for production systems. We also should not be thinking of code as "correct" or "incorrect" because that doesn't measure code quality and security. Additionally, code can be classified as correct through tests but still have major security issues not covered by a test or be something that utilizes too much CPU/memory in production.
But if you actually can specify what the program is supposed to do, this can work. It's appropriate where the task is hard to do but easy to specify. A file system or a database can be specified in terms of large arrays. Most of the complexity of a file system is in performance and reliability. What it's supposed to do from the API perspective isn't that complicated. The same can be said for garbage collectors, databases, and other complex systems that do something that's conceptually simple but hard to do right.
Probably not going to help with a web page user interface. If you had a spec for what it was supposed to do, you'd have the design.
We are simply shuffling cognitive and entropic complexity around and calling it intelligence. As you said, at the end of the day the engineer - like the pilot - is ultimately the responsible party at all stages of the journey.
Each of these approaches is just fine and widely used, and none of them can be called "automated verification", which, if my understanding of the term is correct, is more about mathematical proof that the program works as expected.
The article mostly talks about automatic test generation.