I have a number of such idea, sadly I'm short on time so I never got to write them down in detail. Besides, my friends don't seem to "get" it so I worry I will waste my time inventing the newest developer tools but not being able to gain any traction due to thinking inertia (or me being a crank - you can never tell :) ).
Briefly, I think I understood how to properly build an interactive data-driven application in the highest level of abstraction. If I were build out the tools, one would be able to create such app by defining data models, view models, derivation logic (for deriving view model from data model), viewmodel-bound layout for individual stages, and stage-linking workflow, all in their respective domain-specific languages. It all looks very neat in my head, and I have even programmed a couple of pieces in my iOS apps to great effect.
I have some ideas about how it would work, and have looked a little into how it might be implemented and have some seemingly feasible ideas there too. But I'm not an expert in either NLP or programming language development, so what do I know.
A good first step in this direction is the natural programming language for creating interactive fiction, Inform 7. The problem is that it's not general purpose. But you can try reading about it if you think natural language is too ambiguous to ever possibly be a programming language.
I want to write down all my thoughts about this idea, but I thought I'd get some more meat on it before I do that. Also, I haven't mentioned here why I think natural language programming can help, but I'll leave that to your imagination for now.
This exists. It's "Write each step of an algorithm in comments before writing any code, and (only when you're finished writing each step in English in comments) fill in the code each comment represents".
But it's impossible to convince anyone to try that, though, even though it gives all the benefits you are imagining NLP would give.
The mentality "Don't repeat yourself under any circumstances, even if it adds no complexity and increases clarity!" is unfortunately why this is undiscovered. Also, people generally go overboard with the "commenting" part -- the purpose is to be pseudocode in the way Python is executable pseudocode, while retaining the ambiguity that is necessary for natural language to be natural. So it gets a bad reputation, and its power is hidden despite it being powerful.
The closest experience I've had to this is using RSpec and Capybara to do test-driven development.
Here's an actual snippet of a test I wrote before writing the corresponding code for an app that's now in production:
describe "adding participants" do
context "when user has created a new event" do
before :each do
visit root_path
click_link 'Get Started'
fill_in 'Name', :with => 'Tres'
fill_in 'E-mail', :with => '[email protected]'
click_button 'Next'
end
it "should prompt for more participant info" do
page.should have_field 'Name'
page.should have_field 'E-mail'
page.should have_button 'Add Participant'
end
it "shouldn't let you enter an invalid e-mail" do
fill_in 'Name', :with => 'Tres'
fill_in 'E-mail', :with => '#)(*)($*#)($*'
click_button 'Add Participant'
page.should have_content 'invalid'
end
end
end
It's been an absolute joy to work this way. I'd love to see this level of abstraction make its way into the mainstream in other languages and environments.
Some Lispers have been doing something similar for some time. There's even a saying that "Any sufficiently well-documented Lisp program contains an ML program in its comments."
Literate Programming - another 30 years old "technology". And it's good. As you say, the problem is not the tools, but rather mentality of rediscovering the wheel at best, and staying ignorant to the history of our field at worst.
One way to solve the ambiguity problem of natural languages is using a controlled natural language, which is a limited subset of a natural language[1]. Some of those are machine transformable to logic. There's even some work to encode legal knowledge using them.
Thanks, interesting! Not mentioned on that page, but a notable natural language which is similar in its goals is Lojban [1]. It's actually based on predicate language, which is actually what Inform 7 uses as well in order to derive the actual meaning from natural language sentences. It manages this because it is based on a limited subset of English.
But I'll note that we humans manage to understand each other just fine without resorting to a controlled natural language. We solve the ambiguity problem basically by taking the most likely interpretation of a given sentence, given the context, or asking questions if too confused. And in fact, there are natural language parsers out there that can predict the mostly like parse reasonably well for single sentences.
The biggest problem here is that programmers like having full control of what's going on, whereas this sort of idea brings in a large dose of unpredictability: how will the parser interpret this sentence? I think this can be solved to some extent by having the parser tell you what it parsed in less ambiguous terms. In fact, you might ask the parser to present a normalized version of the code that is more specific about exactly what's going on (and probably more verbose), and also expanding out phrases to show you exactly what they mean for debugging purposes. Also, when a clear parse cannot be made, the parser can simply ask you to clarify, presenting the multiple parses that it could not decide between.
I'd just like to point out that Lojban is a constructed language, not a natural language. It was designed and built from the ground up, as opposed to the GP's controlled natural languages which are pre-existing human languages with a bunch of stuff stripped out of them.
Hm, I have never really thought about NLP, but I guess maintaining an NLP program somebody else has written must be even more of a nightmare than what we have now...
(Just hoping what you have in mind isn't UML + SOAP + some unintelligable über-abstracted-meta-code-gen...)