fake: Randomly generated fake data

[ bsd3, data, library ] [ Propose Tags ]

QuickCheck generates completely random data for the purposes of test and catching corner cases. The fake package provides tools for generating data that looks plausibly real.


[Skip to Readme]
Versions 0.1, 0.1.1, 0.1.1.1
Change log ChangeLog.md
Dependencies base (>=4.6 && <4.12), containers (==0.5.*), generics-sop (>=0.2 && <0.4), random (==1.1.*), text (==1.2.*), time (>=1.4 && <1.10) [details]
License BSD-3-Clause
Copyright Doug Beardsley, Formation Inc.
Author Doug Beardsley
Maintainer mightybyte@gmail.com
Category Data
Home page https://github.com/mightybyte/fake
Bug tracker https://github.com/mightybyte/fake/issues
Source repo head: git clone https://github.com/mightybyte/fake.git
Uploaded by DougBeardsley at Wed Apr 25 16:36:02 UTC 2018
Distributions NixOS:0.1.1.1
Downloads 207 total (19 in the last 30 days)
Rating (no votes yet) [estimated by rule of succession]
Your Rating
  • λ
  • λ
  • λ
Status Docs available [build log]
Last success reported on 2018-04-29 [all 1 reports]
Hackage Matrix CI

Modules

[Index]

Downloads

Maintainer's Corner

For package maintainers and hackage trustees


Readme for fake-0.1.1.1

[back to package description]

Fake

Build Status

Fake is a Haskell package for generating realistic-looking fake data.

Overview

The package has three main components:

  1. Analogs to QuickCheck's Arbitrary and Gen that use realistic probability distributions rather than the more uniform distributions used by QuickCheck.
  2. A generic coverage function that generates full constructor coverage over a data type.
  3. A suite of providers for common types of data such as names, addresses, phone numbers, ID numbers, etc.

Fake's gcover function is particularly useful with the armor package for ensuring that all constructors of your data types are tested for backwards compatible serializations without having to write all the values yourself. This allows you to get higher confidence that you have covered most of the important cases without the combinatorial explosion of exhaustive testing.

Credits

Original inspiration came from the production needs of Formation (previously Takt).

Providers and other details were inspired by similar packages in Python and Ruby.