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Call by Meaning [pdf] (vpri.org)
69 points by da02 on Aug 28, 2014 | hide | past | web | favorite | 8 comments



"In this paper we discussed the large shortcomings in today’s programming practices to support the growing need of harnessing data and software components found on the Internet. The tasks of discovery and ensuring compatibility between components are for the most part done manually, making existing systems hard-to-manage and fragile and building new systems a real challenge."

If a developer wants to understand his/her system as a whole, it's a requirement to understand all included libraries as well. Automatic checks can help a lot, but they must work really well before they can replace manual work. Their work, in particular, adds another language for metadata on top of the existing code, which was self-describing in the first place. When changing the actual code, the metadata description must be changed as well. How do you ensure that the description is accurate? Use yet another specification language and model to check it?


How was the existing code "self describing"? That sounds like what people who don't want to write comments or documentation say, that their code is magically "self describing".


The code is self-describing because it's written in a formal language with precisely defined semantics. By reading it, you should be able to understand what it does.

Here's an "obfuscated" version of the code in from the paper:

  a = {
      b: function() {
          return Math.floor(new Date().getTime()/1000);
      }
  };
This function takes the number of milliseconds since 1970, divides it by 1000 to get the number of seconds, and rounds that value down. There is need for an additional explanation of what the function is doing.

Granted, this is a toy example, and I'm certainly not against comments that describe what the next x lines of complex code are doing. But the paper is about using metadata to help a computer, not a person, to understand what's going on.


I typically code with the manual alongside my editor so syntax lookups are cheap. I prefer dumb and honest APIs coupled with good type systems so that understandable semantics are expressed within the routines themselves.

Therefore, I'm pretty apprehensive about using a system that seemingly encourages ambiguity, e.g. on page 6:

var epochMethodName = K.ask(clock, "what is the name of that's property (methodName) which represents the current time in Unix epoch format?").methodName

If the system is rigid and expects a structurally-similar sentence, then isn't that specification more difficult to remember than simple method names? This could form an unnecessary implicit schema, basically the same as winded function calls.

If the system is extended enough to understand a variety structures conveying the same meaning, what guarantees do I have about maintaining a consistent result? We developed a separate language for mathematics to (among other reasons) eliminate ambiguity, e.g. the exclusive or due to the ambiguity of the word "or". This reminds me of the Dijkstra paper on natural language programming that resurfaced on HN recently:

https://www.cs.utexas.edu/users/EWD/transcriptions/EWD06xx/E...

I don't think within the code itself is the right place to help the computer make sense of my meaning. Such a complex system may mislead the user into thinking that the system is smarter or more capable than it really is, e.g. you can't tell a toaster to K.ask("what's your property (methodName) to bake a cake?"). How is natural language-based service discovery different than a lookup for all APIs implementing the cake-baking interface? I don't think I understand the bridge that the paper makes from natural-seeming language to formal logic.

Instead, I think something along the lines of this proposed system could useful when used while programming. While writing code, I would enjoy a way to look up "a method for this object that does something along the lines of XYZ". I would be interested in a more powerful search engine for documentation that leverages type systems and comment-search more usefully than just word-matching. Right now, the closest thing is Stack Overflow.


I've noticed the problem in my own coding that the first paragraph outlines. Having to manually discover the most useful libraries, maintain the code gluing each of these libraries to the rest of the code.

It's a pain in the neck, doing it once or twice is fine, but once you start getting to around half a dozen projects or so in various states of development / production, going back to change the code on all of them when something changes gets pretty onerous.

I've started to recognize that I basically need to either be willing to either vendor dependencies or build interface classes to separate responsibilities.

So now I maintain an 'extractions' gem that implements common patterns as modules. For example right now I'm building a Persistence module that I can include to persist data. Point it at the instance variable containing the data, stored as a Set of value class instances, tell it which engine to use, and the name of the value class it's persisting, so it knows which columns to make in the table, and it handles all the dirty work automatically, presenting a clean interface to the calling library.

So now I can change or update the persistence library used in the extractions gem, say, by changing ActiveRecord to Sequel or even just separate DB libraries and templated queries, and all of my projects can use the new code with a simple 'bundle update'. Shared conventions, made easier by other extractions, make everything work together fluidly.

Eventually I'd love to get to the point where I don't even have to 'bundle update', running production code could poll the git server daily to see if the extractions gem has changed, then pull the repo and restart the server, the code for which can be stored, again, within the extractions gem.

This could get a little dangerous, so I'm eventually going to build a testing apparatus to test the effects of updating the extractions gem. Spin up a bunch of VMs, provision them, pull the repos, run the test suites and finally start the servers. Any errors get reported.


Precision has to come from somewhere.

Having your actual production code read "call the function that does <this>" and the interpreter/compiler looks it up based on NLP of the documentation seems very silly and fragile and hard to debug.

Having an IDE that can look things up for you (whether full NLP or just keywords) in the API reference and give you a sorted-by-relevance list to pick from, seems like a great idea. (Some already look by substring matching what you type anywhere in the method name, rather than just as a prefix of the name. Which is already pretty good.)


The challenge they envision to address reminds me of a section in Bret Victor's talk, The Future of Programming.

"[13:54]

So, say you’ve got this network of computers, and you’ve got some program out here that was written by somebody at some time in some language; it speaks some protocol. You’ve got another program over here written by somebody else some other time, speaks a totally different language, written in a totally different language. These two programs know nothing about each other. But at some point, this program will figure out that there’s a service it needs from that program, that they have to talk to each other. So you’ve got these two programs—don’t know anything about each other—written in totally different times, and now they need to be able to communicate. So how are they going to do that? Well, there’s only one real answer to that that scales, that’s actually going to work, which is they have to figure out how to talk to each other. Right? They need to negotiate with each other. They have to probe each other. They have to dynamically figure out a common language so they can exchange information and fulfill the goals that the human programmer gave to them. So that’s why this goal-directed stuff is going to be so important when we have this internet—is because you can’t write a procedure because we won’t know the procedures for talking to these remote programs. These programs themselves have to figure out procedures for talking to each other and fulfill higher-level goals. So if we have this worldwide network, I think that this is the only model that’s going to scale. What won’t work, what would be a total disaster, is—I’m going to make up a term here, API [Application Programming Interface]—this notion that you have a human programmer that writes against a fixed interface that’s exposed by some remote program. First of all, this requires the programs to already know about each other, right? And when you’re writing this program in this one’s language, now they’re tied together so the first program can’t go out and hunt and find other programs that implement the same service. They’re tied together. If this one’s language changes, it breaks this one, it’s really brutal, it doesn’t scale. And, worst of all, you have—it’s basically the machine code problem. You have a human doing low-level details that should be taken care of by the machine. So I’m pretty confident this is never going to happen. We’re not going to have API’s in the future. What we are going to have are programs that know how to figure out how to talk to each other, and that’s going to require programming in goals."

http://worrydream.com/dbx/ (quote taken from this helpful transcription of the talk at http://glamour-and-discourse.blogspot.ch/p/the-future-of-pro...)


Can still be an API, but its the 'meaning API' where you probe for common ground, negotiate formats.




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