QuickCheck: a lightweight tool for random testing of Haskell programs
ICFP '00 Proceedings of the fifth ACM SIGPLAN international conference on Functional programming
Journal of Functional Programming
Boltzmann Samplers for the Random Generation of Combinatorial Structures
Combinatorics, Probability and Computing
CUTE: a concolic unit testing engine for C
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
Data-flow testing of declarative programs
Proceedings of the 13th ACM SIGPLAN international conference on Functional programming
Smallcheck and lazy smallcheck: automatic exhaustive testing for small values
Proceedings of the first ACM SIGPLAN symposium on Haskell
Analytic Combinatorics
WISE: Automated test generation for worst-case complexity
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Uniform Random Generation of Huge Metamodel Instances
ECMDA-FA '09 Proceedings of the 5th European Conference on Model Driven Architecture - Foundations and Applications
TLCA'03 Proceedings of the 6th international conference on Typed lambda calculi and applications
Species and functors and types, oh my!
Proceedings of the third ACM Haskell symposium on Haskell
| Hi-index | 0.00 |
Numerous software testing methods involve random generation of data structures. However, random sampling methods currently in use by testing frameworks are not satisfactory: often manually written by the programmer or at best extracted in an ad-hoc way relying on no theoretical background. On the other end, random sampling methods with good theoretical properties exist but have a too high cost to be used in testing, in particular when large inputs are needed. In this paper we describe how we applied the recently developed Boltzmann model of random generation to algebraic data types. We obtain a fully automatic way to derive random generators from Objective Caml type definitions. These generators have linear complexity and, the generation method being uniform, can also be used as a sound sampling back-end for benchmarking tools. As a result, we provide testing and benchmarking frameworks with a sound and fast generation basis. We also provide a testing and benchmarking library, available for download, showing the viability of this experiment.