Symbolica
Symbolica
| Symbolica | Symbolica | |
|---|---|---|
| 4 | 2 | |
| 60 | 39 | |
| - | - | |
| 0.0 | 8.3 | |
| about 1 year ago | over 2 years ago | |
| C# | C# | |
| MIT License | MIT License |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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
Symbolica
What are some alternatives?
Symbolica.Extensions.Configuration.FSharp - Provides a safe API for binding the dotnet IConfiguration to types in F#.
alive2 - Automatic verification of LLVM optimizations
FsCheck - Random Testing for .NET
HDL_Converter - A simple tool that can be used to convert the header syntax of a verilog module or VHDL entity to an instantiation syntax and create testbench structures (top level and verify). The project is aimed at removing the need for tedious refactoring of module headers when instantiating modules or verifying individual modules with testbenches.
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