FsCheck
Symbolica
FsCheck | Symbolica | |
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11 | 4 | |
1,138 | 60 | |
0.8% | - | |
8.0 | 0.0 | |
5 days ago | about 1 year ago | |
F# | C# | |
BSD 3-clause "New" or "Revised" License | MIT License |
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FsCheck
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Property-based tests and clean architecture are perfect fit
As you can see from the imports statement we're relying on FsCheck to generate some random values for us.
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When writing unit tests, what exactly am I looking for?
C# - FsCheck
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Is there a tool that could be used to generate fake unit test cases automatically for code coverage? (read description before downvoting)
https://fscheck.github.io/FsCheck/ can hopefully generate random inputs automatically or with low effort for many methods to get your code coverage up. You don’t even need to write real tests right now, just call the methods with the random inputs and check they don’t fail.
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Does anyone know of a good place to learn and practice some F# preferably F# 6 to be able to use Task.
Try using F# for tests. It has some great libraries like FsCheck (https://fscheck.github.io/FsCheck/).
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Typesafe F# configuration binding
At Symbolica we're building a symbolic execution service that explores every reachable state of a user's program and verifies assertions at each of these states to check that the program is correct. By default it will check for common undefined behaviours, such as out-of-bounds memory reads or divide by zero, but it can also be used with custom, application specific, assertions too just like the kind you'd write in a unit test. Seen from this perspective it's kind of like FsCheck (or Haskell's QuickCheck or Python's Hypothesis), but much more exhaustive and without the randomness.
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Does anybody know a simple algorithm for generating unit tests given a function's code?
Maybe something like QuickCheck, a quick search gave me this library for .NET https://github.com/fscheck/FsCheck
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When do you consider your unit tests be "enough"?
Because of the above I've generally been using tools like Stryker.NET and FsCheck to augment my testing suite. I'm still doing unit testing to find the more obvious "I haven't had my coffee, let's make sure I'm doing what I think I'm doing" bugs. I'm just using things like mutation testing, property testing, fuzzing, etc. to find the deeper issues in my code. There's a ton of libraries out there, including one that I've built for myself to help with testing but FsCheck and Stryker are just beautiful. And if you're interested in fuzzing, SharpFuzz is a great option. But that one isn't quite as easy of an on ramp compared to the other two that I mentioned.
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What are you working on? (2021-06)
Looks cool. Is there a reason why you didn't use FsCheck or Hedgehog? They're built to generate random data for testing, and can return the seed if a test fails so you can rerun the test with the exact same data once you figure out what the problem is - which is useful if the failure condition is rare.
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Mutation Testing
Haskell has QuickCheck and Hedgehog, and dotnet has both as well. F# is favored, but there's C# interop.
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How Good Are Your .NET Tests? Test Your Tests With Stryker Mutator
Side note, if you are thinking about testing in general, might be interested in property based testing. See for example https://fscheck.github.io/FsCheck/
Symbolica
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Typesafe F# configuration binding
At Symbolica we're building a symbolic execution service that explores every reachable state of a user's program and verifies assertions at each of these states to check that the program is correct. By default it will check for common undefined behaviours, such as out-of-bounds memory reads or divide by zero, but it can also be used with custom, application specific, assertions too just like the kind you'd write in a unit test. Seen from this perspective it's kind of like FsCheck (or Haskell's QuickCheck or Python's Hypothesis), but much more exhaustive and without the randomness.
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Symbolica's Console Newsletter Interview
At Symbolica we’re building a cloud-hosted symbolic execution service. Symbolic execution lets you explore every reachable state of your program so that you can write tests without worrying about missing any edge cases. As a bonus we also automatically detect if any states can cause invalid memory access and other undefined behaviours, like divide by zero, without you having to write any additional tests.
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Symbolica’s Console Newsletter Interview
View on GitHub
What are some alternatives?
AutoFixture - AutoFixture is an open source library for .NET designed to minimize the 'Arrange' phase of your unit tests in order to maximize maintainability. Its primary goal is to allow developers to focus on what is being tested rather than how to setup the test scenario, by making it easier to create object graphs containing test data.
Symbolica.Extensions.Configuration.FSharp - Provides a safe API for binding the dotnet IConfiguration to types in F#.
Bogus - :card_index: A simple fake data generator for C#, F#, and VB.NET. Based on and ported from the famed faker.js.
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Expecto - A smooth testing lib for F#. APIs made for humans! Strong testing methodologies for everyone!
Symbolica - Symbolica's open-source symbolic execution engine. [Moved to: https://github.com/Symbolica/Symbolica]
sharpfuzz - AFL-based fuzz testing for .NET
Fluent Assertions - A very extensive set of extension methods that allow you to more naturally specify the expected outcome of a TDD or BDD-style unit tests. Targets .NET Framework 4.7, as well as .NET Core 2.1, .NET Core 3.0, .NET 6, .NET Standard 2.0 and 2.1. Supports the unit test frameworks MSTest2, NUnit3, XUnit2, MSpec, and NSpec3.
hedgehog - Release with confidence, state-of-the-art property testing for Haskell.
Moq - Repo for managing Moq 4.x [Moved to: https://github.com/moq/moq]
CsCheck - Random testing library for C#
GenFu - GenFu is a library you can use to generate realistic test data. It is composed of several property fillers that can populate commonly named properties through reflection using an internal database of values or randomly created data. You can override any of the fillers, give GenFu hints on how to fill them.