David Poll points out the flawed premise of the argument that code review is a bottleneck
To be fair, finding defects has always been listed as a goal of code review – Wikipedia will tell you as much. And sure, reviewers do catch bugs. But I think that framing dramatically overstates the bug-catching role and understates everything else code review does. If your review process is primarily a bug-finding mechanism, you’re leaving most of the value on the table.
Code review answers: “Should this be part of my product?”
That’s close to how I think about it. I think of code review as primarily about keeping the code base healthy. And although many people think of code review as pre-integration review done on pull requests, I look at code review as a broader activity both done earlier (Pair Programming) and later (Refinement Code Review).
At Firebase, I spent 5.5 years running an API council…
The most valuable feedback from that council was never “you have a bug in this spec.” It was “this API implies a mental model that contradicts what you shipped last quarter” or “this deprecation strategy will cost more trust than the improvement is worth” or simply “a developer encountering this for the first time won’t understand what it does.” Those are judgment calls about whether something should be part of the product – the same fundamental question that code review answers at a different altitude. No amount of production observability surfaces them, because the system can work perfectly and still be the wrong thing to have built.
His overall point is that code review is all about applying judgment, steering the code in a good direction. AI raises the level of that judgment, focusing review on more important things.
I agree that we shouldn’t be thinking of review as a bug-catching mechanism, and that it’s about steering the code base. In addition I’d also add that it’s about communication between people, enabling multiple perspectives on the development of the product. This is true both for code review, and for pair programming.
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Charity Majors is unhappy with me and rest of the folks that attended the Thoughtworks Future of Software Development Retreat.
But the longer I sit with this recap, the more troubled I am by what it doesn’t say. I worry that the most respected minds in software are unintentionally replicating a serious blind spot that has haunted software engineering for decades: relegating production to the realm of bugs and incidents.
There are lots of things we didn’t discuss in that day-and-a-half, and it’s understandable that a topic that matters so deeply to her is visible by its absence. I’m certainly not speaking for anyone else who was there, but I’ll take the opportunity to share some of my thoughts on this.
I consider observability to be a key tool in working with our AI future. As she points out, observability isn’t really about finding bugs – although I’ve long been a supporter of the notion of QA in Production. Observability is about revealing what the system actually does, when in the hands of its actual users. Test cases help you deal with the known paths, but reality has a habit of taking you into the unknowns, not just the unknowns of the software’s behavior in unforeseen places, but also the unknowns of how the software affects the broader human and organizational systems it’s embedded into. By watching how software is used, we can learn about what users really want to achieve, these observed requirements are often things that never popped up in interviews and focus groups.
If these unknown territories are true in systems written line-by-line in deterministic code, it’s even more true when code is written in a world of supervisory engineering where humans are no longer to look over every semi-colon. Certainly harness engineering and humans in the loop help, and I’m as much a fan as ever about the importance of
tests as a way to both explain and evaluate the code. But these unknowns will inevitably raise the importance of observability and its role to understand what the system thinks it does. I think it’s likely we’ll see a future where much of a developer’s effort is figuring what a system is doing and why it’s behaving that way, where observability tools are the IDE.
In this I ponder the lesson of AI playing Go. AlphaGo defeated the best humans a decade ago, and since then humans study AI to become better players and maybe discover some broader principles. I’m intrigued by how humans can learn from AI systems to be improve in other fields, where success is less deterministically defined.
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Tim Requarth questions the portrayal of AI as an amplifier for human cognition. He considers the different way we navigate with GPS compared to maps.
If you unfold a paper map, you study the streets, trace a route, convert the bird’s-eye abstraction into the first-person POV of actually walking—and by the time you arrived, you’d have a nascent mental model of how the city fits together. Or you could fire up Google Maps: A blue dot, an optimal line from A to B, a reassuring robotic voice telling you when to turn. You follow, you arrive, you have no idea, really, where you are. A paper map demands something from you, and that demand leaves you with knowledge. GPS requires nothing, and leaves you with nothing. A paper map and GPS are tools with the same purpose, but opposite cognitive consequences.
He introduces some attractive metaphors here. Steve Jobs called computers “bicycles for the mind”, Satya Nadella said with the launch of ChatGPT that “we went from the bicycle to the steam engine”.
Like another 19th-century invention, the steam locomotive, the bicycle was a technological revolution. But a train traveler sat back and enjoyed the ride, while a cyclist still had to put in effort. With a bicycle, “you are traveling,” wrote a cycling enthusiast in 1878, “not being traveled.”
In both examples, there’s a difference between tools that extend capability and tools that replace it. The question is what we lose when we are passive in the journey? He argues that Silicon Valley executives are too focused on the goal, and ignoring what happens to the humans being traveled.
Much of this depends, I think, on whether we care about what we are losing. I struggle with mental arithmetic, so I value calculators, whether on my phone or M-x calc. I don’t think I lose anything when I let the machine handle the toil of calculation. I share missing the sense of place when using a GPS over a map, but am happy that I can now drive though Lynn without getting lost.
And when it comes to writing, I have no desire to let an LLM write this page.
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