fake: Randomly generated fake data
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.
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- fake-0.1.1.2.tar.gz [browse] (Cabal source package)
- Package description (revised from the package)
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| Versions [RSS] | 0.1, 0.1.1, 0.1.1.1, 0.1.1.2, 0.1.1.3, 0.1.2 |
|---|---|
| Change log | ChangeLog.md |
| Dependencies | base (>=4.8 && <4.13), containers (>=0.5 && <0.7), generics-sop (>=0.2 && <0.6), random (>=1.1 && <1.2), text (>=1.2 && <1.3), time (>=1.4 && <1.10) [details] |
| License | BSD-3-Clause |
| Copyright | Doug Beardsley, Formation Inc. |
| Author | Doug Beardsley |
| Maintainer | mightybyte@gmail.com |
| Revised | Revision 2 made by DougBeardsley at 2019-09-22T19:59:58Z |
| 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 2019-05-03T17:46:29Z |
| Distributions | |
| Reverse Dependencies | 1 direct, 0 indirect [details] |
| Downloads | 3213 total (7 in the last 30 days) |
| Rating | (no votes yet) [estimated by Bayesian average] |
| Your Rating | |
| Status | Docs available [build log] Last success reported on 2019-05-03 [all 1 reports] |
Readme for fake-0.1.1.2
[back to package description]Fake
Fake is a Haskell package for generating realistic-looking fake data.
Overview
The package has three main components:
- Analogs to QuickCheck's Arbitrary and Gen that use realistic probability distributions rather than the more uniform distributions used by QuickCheck.
- A generic coverage function that generates full constructor coverage over a data type.
- 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.