The Return of Aspect Oriented Programming

This is, indeed, the next generation of AOP: they've managed to evolve it from "extremely complex and hard to understand runtime behavior" into "completely undefined runtime behavior". UB as a service. True innovation!

At the end of the day aren't we all just non-deterministic process monkeys patching code?

After reading this post, I got curious and went back to read the original AOP paper. What the OP argued feels like just top-down design. These days, the term 'SPEC-driven' is trending, but it seems like just another word for top-down. They're probably just rebranding it because top-down has a negative image.

Originally, AOP was about separating cross-cutting concerns by centralizing them in one place. It used weaving to separate infrastructure code, and implicitness was inherent in that approach. But the books I read back then said this led to 'ghost code.' It inevitably introduced unpredictability because the behavior wasn't visible in the code. And from a programmer's perspective, that becomes a problem when things break.

On top of that, while the cross-cutting concerns are centralized, they still end up being tied to the framework's syntax, like Spring AOP's Join Point syntax, so they become dependent on the framework itself.

That's why DDD became popular as another way to address OOP's limitations. DDD keeps the business logic pure and framework-agnostic, and that's where things like POJO emerged. At least that's what I read in books, just different approaches to the same problem.

AOP was first presented at ECOOP in 1997, and DDD is usually associated with Evans' book. Both are ways of handling OOP's complexity, but the article here doesn't seem to talk about problems with cross-cutting concerns, which is the core of AOP at all. And this is something AI doesn't handle well. AI makes the most mistakes with implicit knowledge.

Or maybe I'm wrong because I've been studying programming history through older books and have outdated knowledge. Maybe AOP has evolved since then.

What makes it difficult to talk about specific 'oriented' paradigms in programming is that as history moves on and certain problems get solved, if you bring up an older version, you'll get pushback from people who really know their stuff.

'That's a problem that was solved 5 to 10 years ago.' 'That issue has evolved and been covered in other books.'

So it's hard to talk about any 'oriented' approach because you have to specify which era's version you're referring to. For example, even with OOP, which everyone knows, there's a big difference between Smalltalk, C++, and the modern emphasis on composition over inheritance. Someone might say, 'Modern OOP is centered around composition and value objects — you're behind the times.'

AOP might have also evolved and introduced different solutions since then.

So while the perspective on a given 'oriented' paradigm does shift over time, it's really hard to have a conversation about programming because it all depends on which era the programmer is from and how far their knowledge goes.

That's why lately I've been thinking about problems and the approaches used to solve them, rather than focusing on the 'oriented' labels. I wish someone would write a history book about these paradigms. They'd get a lot of criticism, but for programmers like me, it would be much easier to understand.

Sometimes I think it's about time someone wrote a history book on programming

The idea of this post - to write separate requirements for each concern and let LLM's integrate them - is much closer to the Leo version of literate programming, which allowed documents to be composed (roughly via scatter/gather operations mediated by sentinels).

But the post entirely lacks the motivation for the AspectJ/AOP join point model: to have principled time/place for concern integration that was statically determined, type-safe, understandable to users -- and suitable for integration.

> I've also always hated the specific mechanism that AOP chose to implement it with – something called the "join point model" which basically amounts to runtime pattern matching on a program's call stack and running some code every time a pattern matches.

AspectJ's join point model is only dynamic where Java as a reference-based language could not support the static analysis. At compile-time, the "static shadow" of the pointcuts was calculated and implemented where staticly determinable; only the dynamic residue is deferred to runtime (e.g., is the caller to this method of type X?).

Many of AspectJ's join points and type extensions - method call or execution, exception throwing, field access - largely have been adopted in many languages (python context managers, swift getter/setters/extensions), and the residue are a bit hard to use.

But nothing really matches the power of pointcuts: to combine these predicates and the type-safe state-management - e.g., "when throwing an exception after a transaction, capture the span id along with the user id into a log message"

AOP was great for the 7% of code that it was intended for, but was largely displaced as too complicated. Now with LLM's it's a decent hypothesis that with proper training LLM's could actually handle the more complicated but ultimately cleaner programming model - cleaner because it avoids the scattering of similar code which makes it hard to change.

The key insight is that dominant concerns establish the basic structure of the application, leaving some important but residual aspects to fit themselves to the structure. That means the dominant structure must be suitable for the AOP integration (i.e., support the right pointcuts and type extensions); solve that and you've solved most integration issues. It's especially helpful for feature architectures, where you offer code in open-source to gain API adoption, paid for by closed-source library integrations with additional features.

re: dissolving into compilation, I think the machine/human separation has been at work for some time. Modern languages (e.g., rust, swift) are already pioneering tracking aspects like effects, lifetimes, regions, etc. and then using whole-program optimization at compile- and link-time, largely based on intermediate languages like LLM IR/SIL, which are surfaced as user-visible features when they compose well with other user-visible language features. LLM training on these languages makes them suitable for generative AI; I doubt LLM's could pick up some new language, particularly if it weren't analogous to existing ones.