Composing generators

@composed

While Supposition.jl provides basic generators for a number of objects from Base, quite a lot of Julia code relies on the use of custom structs. At the innermost level, all Julia structs are composed of one or more of these basic types, like Int, String, Vector etc. Of course, we want to be able to generate & correctly shrink these custom structs as well, so how can this be done? Enter @composed, which can do exactly that. Here's how it's used:

using Supposition

const intgen = Data.Integers{Int}()

makeeven(x) = (x÷0x2)*0x2

even_complex = @composed function complex_even(a=intgen, b=intgen)
    a = makeeven(a)
    b = makeeven(b)
    a + b*im
end
example(even_complex, 5)

5-element Vector{Complex{Int64}}:
  8719933327026013518 + 564649821030611010im
 -1010666256572487984 - 8060239345742109948im
 -4596581375590991704 + 614960869535584746im
  2881657790005231444 - 2539481425773804608im
  8215413007108111084 - 1933164924104869402im

In essence, @composed takes a function that is given some generators, and ultimately returns a generator that runs the function on those given generators. As a full-fledged Possibility, you can of course do everything you'd expect to do with other Possibility objects from Supposition.jl, including using them as input to other @composed! This makes them a powerful tool for composing custom generators.

@check function all_complex_even(c=even_complex)
    iseven(real(c)) && iseven(imag(c))
end

In addition, @composed defines the function given to it as well as a regular function, which means that you can call & reuse it however you like:

complex_even(1.0,2.0)

0.0 + 2.0im

Filtering, mapping, and other combinators

filter

Of course, manually marking, mapping or filtering inside of @composed is sometimes a bit too much. For these cases, all Possibility support filter and map, returning a new Data.Satisfying or Data.Map Possibility respectively:

using Supposition

intgen = Data.Integers{UInt8}()

f = filter(iseven, intgen)

example(f, 10)

10-element Vector{UInt8}:
 0x1e
 0xe8
 0xd6
 0x98
 0x56
 0x1c
 0xe6
 0x8a
 0xa8
 0xea

Note that filtering is, in almost all cases, strictly worse than constructing the desired objects directly. For example, if the filtering predicate rejects too many examples from the input space, it can easily happen that no suitable examples can be found:

g = filter(>(typemax(UInt8)), intgen)
example(g, 10)

ERROR: Tried sampling 100000 times, without getting a result. Perhaps you're filtering out too many examples?

It is best to only filter when you're certain that the part of the state space you're filtering out is not substantial.

map

In order to make it easier to directly construct conforming instances, you can use map, transforming the output of one Possibility into a different object:

using Supposition

intgen = Data.Integers{UInt8}()
makeeven(x) = (x÷0x2)*0x2
m = map(makeeven, intgen)

example(m, 10)

10-element Vector{UInt8}:
 0xc2
 0x78
 0x98
 0xca
 0x08
 0x20
 0x42
 0xde
 0x38
 0xa2