| I have thought about this before but I can see that logical errors are introduced which must be manually tested and reviewed anyway, so what if a more reliable approach could be achieved by training these data sets on test cases alongside passing code? This way developers just write unit tests or functional tests, and the AI generates code and retrains itself until the code passes for all tests. This could happen silently in the background as the developer defines the tests. A number of natural language test frameworks exist, Behat for example lets you define tests such as: Feature: Multiple site support Background:
Given a global administrator named "Greg"
And a blog named "Greg's anti-tax rants"
And a customer named "Wilson"
And a blog named "Expensive Therapy" owned by "Wilson"
Scenario: Wilson posts to his own blog
Given I am logged in as Wilson
When I try to post to "Expensive Therapy"
Then I should see "Your article was published."
Scenario: Greg posts to a client's blog
Given I am logged in as Greg
When I try to post to "Expensive Therapy"
Then I should see "Your article was published."
It could still fit the dream of describing to a computer what kind of program you want and having it figure out the plumbing.Anyway interesting work. Very interesting. I remember a few colleagues laughed at me no more than 5 years ago when I suggested that AI would eventually write code. And here it is, in an early version, flawed surely but only set to improve. Edit to add: This subject while insanely interesting to me is well out of my wheelhouse. I'm guessing there's possibly semantic structure to the above that the type of model being used in the demo can't deal with? Like this one use-case has to co-exist in an entire ecosystem of dependencies and related entities... Could the model cope with that or is it just calculating the likelihood of the next character like other models I've seen, but with insane accuracy when it comes to code? |