AltWalker is an open source, Model-Based Testing framework for automating your test execution. You design your tests as a directed graph and AltWalker executes them. It relies on GraphWalker to generate paths through your graph.
Model-Based Testing is a testing technique which offers a way of generating test cases based on models that describe the behaviour (functionality) of the system under test.
The goal when designing models is to represent the part of the system under test, usually by one model for each functionality of your system.
With the help of graph theory we can dynamically generate multiple test scripts. A test script is a path passing through the model from a starting point till a condition is met.
Why use Model-Based Testing:
the abstraction layer added by the model gives your tests a better structure
the model can be updated to reflect the requirements changes making the tests easy to maintain
dynamically generates multiple test scripts based on different conditions (like coverage or length)
allows for a large number of tests to be created which results in a larger part of the system under test to be covered.
AltWalker is a test execution tool, which aims to make it easy to write and run your model-based tests. AltWalker uses GraphWalker to generate a path through the models.
For the test structure it uses an Object-Oriented approach inspired by python’s
unittest module. Every model is mapped to a class with the same name and each vertex and edge from the model is mapped to a method inside the class.
AltWalker also borrows the concept of test fixture from unit tests, and implements the following fixtures:
Now it supports running tests written in .NET/C# and Python3.
AltWalker has the following components:
Model: a directed graph, supplied by the user as a json or graphml file. A graph is composed from a list of vertices and a list of edges.
Generator and Stop Condition: used to specify how to generate a path and to decide when a path is complete.
Test Code: the implementation of the model(s) as code. Each model is mapped to a class and each vertex and edge is mapped to a method.
Planner: uses the model(s) and a pair of generator and stop condition to provide a path (a sequence of steps) through the model(s).
Currently AltWalker provides two planners:
Reporter: reports the output of the tests, the reporter is called on each event (e.g.
Executor: for each step in the plan it looks up and calls the named method from the test code. In addition to the step methods, it also calls fixture methods if present (e.g.
Currently AltWalker provides three executors:
Python Executor (Built-in)
And an Http Executor that allows you to hook up your own executor via HTTP. You can read more about the Http Executor on the How to: Write your own executor page.
Walker: the test runner. Coordinates the execution of a test asking the
Plannerfor the next step, executing the step using the
Executorand reporting the progress using the
There are two ways to run your tests:
Online Mode (using the Online Planner): Generate one step and then execute the step, until the path is complete.
Offline Mode (using the Offline Planner): Run a path from a sequence of steps. Usually the path is generated using the
GraphWalker is an Model-Based Testing tool. It reads models in the shape of directed graphs, and generates (test) paths from these graphs.
AltWalker uses GraphWalker as a Planner (it uses the REST API as the Online Planner), so it inherits the model(s) formats (graphml and json) and the generators and stop conditions.
AltWalker also uses the
check commands from GraphWalker.
offline command form GraphWalker is used by the AltWalker’s
offline command, it takes
the output and saves it in a file as a list of steps.
check command from GraphWalker is used by AltWalker’s