Hello, This library aims to derive random generators for ADTs. The previous and first release was an implementation of Boltzmann generators, in response to this blogpost by Brent Yorgey. https://byorgey.wordpress.com/2016/03/23/boltzmann-sampling-for-generic-arbi... It's nice to look at, but it is an opinionated choice about the resulting probability distribution which is not always suitable. In this release, using GHC.Generics, Generic.Random.Generic wraps common boilerplate to define "arbitrary" in particular, hiding from you long chains of the following: Constructor <$> arbitrary <*> arbitrary <*> arbitrary as well as frequency [(n1, C1 <$> arbitrary), (n2, C2 <$> arbitrary), ...] leaving just the weights [n1, n2, ...] for you to specify. data Tree a = Leaf a | Node (Tree a) (Tree a) deriving Generic instance Arbitrary a => Arbitrary (Tree a) where arbitrary = genericArbitraryFrequency [9, 8] -- equivalent to -- > arbitrary = frequency -- > [ (9, Leaf <$> arbitrary) -- > , (8, Node <$> arbitrary <*> arbitrary) -- > ] It comes with a simple opt-in strategy to bound the size of generated data, especially convenient for recursive data types. Indeed, even for a simple binary tree, the naive generator obtained by producing a leaf half of the times, and an inner node the other half, with two recursively generated children, results in a random tree of *infinite* expected size (though the tree is still "almost surely" finite). There is no theoretical reason the library uses two flavors of generics for different tasks. There are trade-offs, but I can't see impassable obstacles to use one in place of the other. If I had to be consistent now I'd choose GHC.Generics, though I was not fluent enough in that flavor at the time when I wrote the Data part. Property-based testing is the main application I have in mind for this library. Nevertheless, I welcome ideas, questions and suggestions from any domain or of general nature. Github: https://github.com/lysxia/generic-random Hackage: http://hackage.haskell.org/package/generic-random Li-yao