The Return of Aspect Oriented Programming

Looking at transactions: The 99% solution is trivial: Every service call is a transaction. AOP can save me a few lines for every method and things look much cleaner.

But then comes the huge excel upload that is performance critical. Batch more service calls to fetch additional information in the background, commit every so-and-so records in a loop depending on the data size, do a custom roll-back if things fail.

And suddenly this whole separation of concerns breaks down and creates a huge mess.

The simple case saves a few minutes, the complicated case causes weeks of depression. Not a good tradeoff from my experience.

An LLM adding to the confusion by only sometimes getting things right and explaining that the separate documents are always valid, except when they are not, well, sounds like a fun experience.

And me, like others have tried structuring our code like this, and failed, assuming the fault lay not with the idea itself but our skill level. Of course, by now it's kind of common knowledge that inheritance isn't a thing that can and should be used to solve every kind of problem.

Same thing with AOP - it might be sometimes nice, but on the whole, elevating this to the language level seems to be counterproductive.

If only.

Application of inheritance is a reaction to the current state of the code, not a foundation you start with.

There are a limited number of patterns that absolutely do benefit from AOP though. The obvious one is logging. I don't think there's many though.

Regardless, AOP is the last thing I'll be using these days. With LLMs I've been moving in the opposite direction with a focus on explicitness and correctness. Typed, compiled, non-null languages with clear, obvious, and well documented conventions.

Aha! That's exactly the sort of thing that can make code impervious to LLM AI.

LLMs have no grasp of any issues that are not visible in syntax, like concurrency. Code that has deadlocks or race conditions (because of other code not seen elsewhere) can look right, but be wrong.

In other words, if you write a program using convoluted spaghetti logic full of invisible data members and control flows injected remotely by aspects, LLMs trained on the code will have no understanding of it; they will just predict tokens according to the naive, visible code.

Imagine if all code out there available for training LLMs was heavily AOP. LLMs trained on it wouldn't be worth a damn. They would not correctly crib the entire solution: generate the code, and bring in the invisible aspects needed to actually make it work. All their solutions would just be the naive surface code that must be invisibly instrumented by half a dozen aspects to be complete.

AOP-heavy code would have to be somehow cleverly preprocessed for training in order for token prediction to do meaningful things with it.

I completely agree with you, saved stuff is normally trivial, nightmare it can bring down the line makes those war stories that are fun to listen to, but certainly not fun to walk through. I simply skip them despite ie Spring offering powerful ways to manage transactions, logging etc. decoupled from places things are actually happening.

I can imagine it working well in a disciplined team who consists of senior folks knowing their craft. Certainly I have never been part of a team with only such composition.

That, funnily, could be the motto of Forth

Not always. DTrace, for example, is a tool to use AOP with programs and/or the OS kernel that makes the normal cases trivial (https://en.wikipedia.org/wiki/DTrace#Command_line_examples) and the hard cases possible (examples at https://github.com/opendtrace/toolkit)

By carefully limiting what code you can inject, it prevents you from accidentally making hard cases hard to reason about.

Oh HELL NO.

The LAST thing you want is a non-deterministic process monkey patching your code.

We need to stop the pseudophilosophy already. At this point, the AI bros think they have cornered some grand problem. If the universe is deterministic, then we can simulate it perfectly and humans and their other machines are all redundant. If the universe is non-deterministic, then stochastic AI "will eventually get better" and can replace humans too.

Have you considered there's something obviously missing with this reasoning, and you're wrong about this like the rest of us? This is not that profound.

  > The LAST thing you want is a non-deterministic process monkey patching your code.

I'm unclear on AOP in general, esp as proposed here. That's a bigger leap...

I think I prefer Springboot AOP, especially with SpEL.

The term “cross cutting concerns” is thrown around a lot when discussing AOP. Took me a while to appreciate just how powerful it is in this context - sprinkle an AOP annotation here or there to avoid massive refactors in a large codebase, or avoid rewriting classes in a way that makes your classes themselves “cross cut concerns.”

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

- Want to add a new method? Maybe it's covered by an existing joinpoint expression and pulled into a pointcut, before you've even written it.

- Want to rename something? Where is it referenced? You can't just search for its name as a whole-identifier match, but must find every join point expression that could contain a regular expression match for it, and then filter out those whose other conditions don't match (wrong class, or whatever).

It is really hokey; and AOP tooling is possible without it, just more cumbersome.

E.g. we can have a special annotation which indicates that method is a joinpoint target, written somewhere on the method. That annotation can list the pointcuts to which it belongs. That becomes more maintainable. The reader of the code knows that since the method is a joinpoint, it interacts with certain pointcuts. They are listed by name, so you can jump to their definitions. When someone adds a new method to that class, they will see that the existing methods have this cruft on them, and decide whether to crib it, and how much. Just because the new method has a certain name doesn't mean it should be in those point cuts, or maybe not all of them.

I really enjoy hearing these kinds of war stories from senior programmers. I don't have a LISP background, but having learned Haskell, your point resonates perfectly.

In the Haskell mindset, letting string-matching rules alter the actual semantics of a program is fundamentally wrong. (At least, that's how I'm interpreting your LISP analogy since I don't know the LISP side well.)

I completely agree with you. Changing the meaning of logic based on string patterns is definitely an anti-pattern. (Though, to be brutally honest, when deadlines are incredibly tight, I've definitely caught myself mindlessly doing it too.)

Ah, about that; regarding OOP, the "that problem has been solved" goes backwards in the revisions. Newer OOP breaks things. As in, "Ah, we have had that problem in Java since 2014, but did you know it was solved in Smalltalk 80". :) :)

It's possible that what you have here is an idea for what I consider to be eventually very likely, which is a computer languages still built for humans to be able to understand and debug it, but more primarily for LLMs to write it. Write a language designed to be an aspect-oriented language from the beginning. Equip it with the ability to run something like a language server and point it at a system and get all the "aspects" running and you might have something.

But I'm skeptical of bodging this on to an existing language.

One of the reasons I suggest making it a new language is that AOP was hampered by being able to use only what languages already supported. The need for a "weaver" is a smell anyhow. Something where the aspect code is the native representation and the "weaving" simply dissolves into the compilation process would not only make the whole thing more appealing in general, I think it would also allow for some things that even code generation might have found a challenge, like aspects that can maintain guarantees because the whole process is more aspect-aware and not broken by the embedded "payload" code written by a human.

Any new language, of any kind, optimized for LLMs or not, is going to intrinsically take multiple LLM training cycles to grow anyhow. Net-net I would still expect AI adoption to accelerate those things and at least make it easier for hobbyiests to play with at some useful scale and get more feedback faster. Of course they'll also face increased competition from the other languages riding the same waves, which is possibly the bigger problem for anyone thinking of doing this.

LLMs basically solve the classic Frame problem that prevented general problem solvers to be able to reason logically about the real world; however on their own they are utterly unpredictable and unreliable.

However if the database of weights is merely used as a heuristic to guide the logical reasoning engine to promising regions of the problem space, and the program itself is written to specification directly by an inference engine, the result would be classic software not affected by hallucinations.

The LLM could even help debugging the specifications by pointing out unclear or contradicting requirements, improving the process without compromising the integrity of the result.

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.

Also one would say monkey patching on Python and Ruby frameworks is another way to do AOP.

IIRC—and it may have changed in the several years since I made use of it for this, but I don’t see why it would—the standard way to do AOP in Ruby is leveraging modules-as-mixins which are a core language feature, monkey patching is unnecessary (but since classes in Ruby are open, modules-as-mixins can be used to monkey patch classes provided by someone else just as easily as being done at class definition time.)

Aspect-oriented programming has annoying implementation in languages like Java which don’t natively support the right abstractions and where you are fighting the language to do it.

Its kind of unfortunate that those are also the languages also which do the most to shape people’s understanding of AOP.

I would say not having language support is a king of way of language designers not approving of its widespread use.

If anything Java and .NET are slowly closing some avenues that make patching possible, like final really means final JEP in Java.

While the robust dynamism of Ruby (or Python) can allow it to be used in a patching-like way, the only dynamism that is essential to the implementation is the dynamism of runtime polymorphism (common in statically-typed, AOT-compiled OO languages.) The language feature it relies on that C++ and Java don’t have is multiple inheritance that gets resolves to a defined order you can call back up from a concrete method with “super”. There is no reason a static OOP language that supports runtime polymorphism couldn’t support this; it wouldn’t even need to add overhead to compiled classes that don’t make direct super calls from their own methods.

And one giant problem with it is the reliance on global variables. An AOP wrapper has to modify something, and it typically does not have enough access to enough context to do it.

So it has to rely on ambient data that has to be saved in a global variable. And this is _bad_. It makes data flow opaque and impossible to follow.

And then there are issues with debugging. Where do you put a breakpoint? What happens if you try to step into an instrumented method?

PS: yes, a global variable can technically be a thread-local variable. It doesn't matter, it's still a non-local ambient state.

It's really neat:

https://micronaut.io/2019/10/07/micronaut-aop-awesome-flexib...

- code readability and maintainability takes a hit. If you don't know things are defined using AOP in files x,y,z you can read the code and miss a whole lot of things.

- AOP implemented at runtime is a mess when you're trying to debug things

So yeah, instead of having aop defined somewhere else to wrap a function call, i tend to prefer doing it explicitly transaction(function())

LLMs really like the most important context to be clustered in the most recent section of their window. Dividing up cross-cutting concerns into their own documents would seem to be pushing in the other direction.

Rather, I got to rely on the special handling within, notably at the time, the Java JEE ecosystem. It was not "generic" AOP by any means, it was specialized. But I can say that what they did support was more than enough for my applications. I never felt I could use a more flexible framework.

I know some folks that did, the embraced it whole hog, and really got some power out of it. AOP could be used to make some very powerful constructs, especially adding dynamic behaviors to existing systems.

But that was simply never something I really needed in my Java work.

Our approach was quite a bit different to Kiczales at the time. We had "attribute providers" which were essentially compiler plugin DLLs.

These plugins could register themselves at various parse points and add work like enzymes on the AST.

So here we have a compile time attribute that writes all the boilerplate to add windowing support to a class. Adding message maps, hooking up the dispatch, life cycle etc.

[Window] class MyWindow { };

It worked, but as others pointed out, it suffers from combinatorial explosion, and issues with debug ability.

That said, as long as you stick within well defined verticals it was still very useful and saved a lot of typing, and reduced cognitive load.

I have thought quite a lot about modern versions of this using LLMs and I can see why the article notices a parallel to prompts or spec based designs.

At some point I tried to make a version of the system we built almost 30 years ago, but using an LLM as the preprocessor instead of rigid AST hackery.

It works a lot better, and you can approach a more continuous interpolations between intent blocks and generated code. Even the combinatorial problem largely disappears.

I stopped working on it though because I couldn't really see anyone wanting to adopt a new language or some whacked out extensions these days.

Maybe it does have its place though in certain fields. It might be worth having another go at it with fresh hindsight.

On the 'experimental' side you have Jonathan Blow's language, Jai, which has integrated codegen (AST macros that look like code) + type inference into the language on a very deep level, and from the podcasts I've listened to with veteran C programmers, was that during the 90s, when the mass adoption of OOP began, but performance still mattered a lot - there were a lot of ideas around OOP that were different from how C++ ended up doing this.

The most famous example I guess being COM, which is a C object model, that solves a bunch of issues that plague C++ to this day, such as reflection and code reusability, among others.

But COM is C and entirely incompatible with C++. And the big issue imo with C++ is that like AOP here, it has elevated a bunch of arbitrary magic behavior to language level, that honestly could've been done very differently, and the C++ implementation often ends up worse (multiple inheritence is a typical example).

A similar battle played out in Linux-land, with GTK creating its GObject system, which was roughly analogous to COM, and Qt opting to hack up C++, not unlike MFC.

Doubts in comments so far seem around expectations of an AOP-like experience. Mainly that patching aspects/Spec makes happy changes to implementation. I'm not sure TFA intended that use. Nor that current AOP implementations provide the happy. But lets explore.

With a frontier model, a greenfield ends up with, say, some spec and plan docs, and a "woven" implementation. It's reasonable to wonder how well an LLM will then un-and-re-weave code as it makes changes. And how to express join points or equivalent. And how well code review agents will detect/check intended joins continuing to be woven correctly. And whether a baseline of the LLM simply using existing AOP tooling constitutes progress.

Coming from non-frontier models, some of that seems more straightforward. Harnesses use lots of small jobs/tasks, with subagent paperwork and reviews. Historically scarce context, precious context, encouraged tightly optimizing single-task context for each job. Including derived code "views", rather than raw code. And associated LLM-coded tooling around working with not-the-raw-code.

Thus working with name/signature pseudocode outlines. And filtering code/ast. Both get you closer to having unwoven forms, and to deterministic tooling for un/re-weaving. Consider i18n clutter - just to coddle the model, one might strip it (replace it with English), let the model work, and then restore it. Which is equivalent to un/reweaving an i18n aspect. And even read-only views, like function distillations which drop or abstract some specified variables and associated code, make that "check spec against implementation" code review easier. So, even interpreting TFA broadly, it seems at least potentially possible, no?

Big picture, I'd like to get away from traditional "repo as single point in design space, laboriously nudged around", to something which preserves the multiplicity of model/run/prompt ensembles, and allows playing on design space manifolds. "This exact code has been in prod" is useful information, if underutilized, but not so useful as to be worth discarding so many new possibilities.

At first it hooks you these cross cutting concerns, but then you realize it handled better as a simple dependency.

AOP as a concept is great, however in practice using real world tools never lived up to the implications.

Its all or nothing, almost if not all code can be written as a "cross cutting concern"

I could write one function for the rest of my life, and simply use AOP to inject a different program.

but WHY would I do that? Something so incongruous with my mission as a software engineer. AOP was pretened smalltalk MVC glue for other languages that didnt have it at the time.

It didnt last long and it wont be back.