inconsist

inconsist

canonical-data.json needs standardisation

Hello,

I maintain the Factor track, 
and I'd like to automate generation of unit tests 
for exercises in my language, and looking at 
`exercises/leap/canonical-data.json` it would seem 
to be quite simple. However, many of the 
`canonical-data.json`s don't have a standard set 
of keys found in `leap`'s json, and this makes it 
difficult to automate around.

There are, as far as I can tell, two solutions to 
the problems introduced by the inconsistencies.

* Rather than hardcoding the `description`, `input` 
and `expected` keys, use a regex / fuzzy find to 
group keys into description, input and output.
The main disadvantages of this are twofold: not 
only must my code be flimsy, but so must everyone 
else's, and subject to break on the whims of anyone.

* Standardise on a fixed, predictable set of keys 
and what their values represent. This makes the jobs
of track maintainers easier, simplifies interacting 
code, and future-proofs the api and the code.

I think standardisation would be greatly beneficial,
but before I open a pull request with structural 
changes to hundreds of lines of data, I'd like some
feedback.

First, is anyone objected to changing the names of 
the keys? They're rather haphazard (nearly as if
it had been written for humans to read ): ) and some 
exercises are missing `canonical-data.json` altogether, 
and consequently I have difficulty believing there are
programs reading this stuff. (If we make an API more 
accessible, perhaps more tracks will automate 
generation / regeneration of tests, which would be 
positive.)

Second, what keys should be used? I'm thinking 
something like:

* For exercises with one input translating to one
output, `description`, `input` and `output`.

* For exercises with multiple inputs / multiple 
outputs, `description`, `input_N`, `output_N`. 

Note that it would be disadvantageous to use an array 
for multiple inputs / outputs where one is not part of the 
exercise because it would be hard or impossible to tell the 
difference.