Deriving Compositional Random Generators
Paper in proceeding, 2019

Generating good random values described by algebraic data types is often quite intricate. State-of-the-art tools for synthesizing random generators serve the valuable purpose of helping with this task, while providing different levels of invariants imposed over the generated values. However, they are often not built for composability nor extensibility, a useful feature when the shape of our random data needs to be adapted while testing different properties or sub-systems.

In this work, we develop an extensible framework for deriving compositional generators, which can be easily combined in different ways in order to fit developers’ demands using a simple type level description language. Our framework relies on familiar ideas from the à la Carte technique for writing composable interpreters in Haskell. In particular, we adapt this technique with the machinery required in the scope of random generation, showing how concepts like generation frequency or terminal constructions can also be expressed in the same type-level fashion. We provide an implementation of our ideas, and evaluate its performance using real world examples.

Haskell

type-level programming

random testing

Author

Claudio Agustin Mista

Chalmers, Computer Science and Engineering (Chalmers), Information Security

Alejandro Russo

Chalmers, Computer Science and Engineering (Chalmers), Information Security

ACM International Conference Proceeding Series

Vol. 25 September 2019
978-145037562-7 (ISBN)

31st Symposium on Implementation and Application of Functional Languages
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Areas of Advance

Information and Communication Technology

Subject Categories

Embedded Systems

Computer Science

Computer Systems

DOI

10.1145/3412932.3412943

ISBN

9781450375627

More information

Latest update

1/31/2022