Package: py-artest
Artest: Auto Regression TestImagine you have a software program, and you want to create a regression test case for a specific function within it.
🔬 To achieve this, it's best to simulate real-life scenarios.
🌍 This is where Artest, a useful tool, comes into play.
Artest automates the process of generating test cases while your program is running.
🤖 These test cases are then stored in a designated directory.
📂 When you make future modifications to your program,
you can rely on the previously generated test cases to ensure that everything continues to work smoothly.
👌 This approach safeguards the stability and dependability of
your program by preventing unexpected issues and accidental breakdowns. 🚀InstallationYou can install Artest using pip:pipinstallpy-artestThis will install Artest and its dependencies.
Optionally, if you require the 'dill' package for additional functionality,
you can install it as follows:pipinstallpy-artest[dill]TutorialYou can find a detailed tutorial on how to use Artest in the following articles:Simplifying Regression Testing in Python with PyArtest’s DecoratorsGist Sample CodeAdvantagesAutomation 🤖: Artest automates the process of generating test cases for your software program while it is running, saving you time and effort in creating them manually.Real-life Scenarios 🌟: Artest allows you to simulate real-life scenarios for regression testing, enabling you to validate your program's behavior in a more realistic context.Test Case Storage 📂: Artest stores the generated test cases in a designated directory, making it easy to access and manage them.Regression Testing 👨‍💻: By relying on previously generated test cases, Artest enables you to perform regression testing. This means you can run the tests after making future modifications to ensure that the program continues to function correctly, catching any unexpected issues or breakdowns.Stability and Dependability 🚀: Utilizing Artest's generated test cases helps safeguard the stability and dependability of your program. By identifying and preventing issues early on, it ensures that your program remains reliable and performs as expected.Basic use case: autoregLet's consider a program written in Python:defhello(say):to=to_whom(1)returnf"{say}{to}!"defto_whom(x):choices=read_from_db()# choices = ["sir", "world",]returnchoices[x]if__name__=="__main__":print(hello("hello"))# Output: hello world!In this program, you only need to add a decorator called autoreg to the hello function:fromartestimportautoreg@autoreg("a5f4cb0f")# 🎉 add this to auto create test casedefhello(say):to=to_whom(1)returnf"{say}{to}!"defto_whom(x):choices=read_from_db()# choices = ["sir", "world",]returnchoices[x]if__name__=="__main__":print(hello("hello"))# Output: hello world!By applying the autoreg decorator,
you enable the creation of a test case associated with the
unique function ida5f4cb0fwhenever the hello function is called.
The test case generation is done automatically.
In this updated version, we still have the to_whom function to determine the value of to.
When running the program, the output remains the same: "hello world!".Stubbing with autostubIn situations where the to_whom function takes a long time to execute or is not available during testing, you can create a stub instead. A stub is a simulated function that returns predefined values specifically defined in the test case.To automatically create a stub, you can use the autostub decorator. When applied to a function, Artest will generate the stub for you.fromartestimportautoreg,autostub@autoreg("a5f4cb0f")defhello(say):to=to_whom(1)returnf"{say}{to}!"@autostub("35988d25")# 🎉 add this to auto create stub functiondefto_whom(x):choices=read_from_db()# choices = ["sir", "world",]returnchoices[x]if__name__=="__main__":print(hello("hello"))# Output: hello world!In the updated code, the to_whom function has been decorated with autostub
using the unique identifier "35988d25".
This allows Artest to automatically generate a stub for the to_whom function.
Inside the stub, the choices variable is typically defined in the test case
rather than being fetched from the database,
ensuring faster and more controlled testing.When running the program, the output remains the same: "hello world!".Test with artestOnce artest is installed, you can run it in test mode using the command:python-martestThis command executes artest in test mode,
allowing the framework to manage and execute the defined test cases
based on the configured environment.
Running artest in test mode enables the verification and
validation of your program's behavior against the predefined test cases,
ensuring that the functionality operates as expected
and remains stable even after modifications.