Skip to main content

Automated Regression Testing Framework for Python

Project description

Artest: Auto Regression Test

Imagine 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. 🚀

Installation

You can install Artest using pip:

pip install py-artest

This will install Artest and its dependencies. Optionally, if you require the 'dill' package for additional functionality, you can install it as follows:

pip install py-artest[dill]

Tutorial

You can find a detailed tutorial on how to use Artest in the following articles:

Advantages

  • Automation 🤖: 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: autoreg

Let's consider a program written in Python:

def hello(say):
    to = to_whom(1)
    return f"{say} {to}!"

def to_whom(x):
    choices = read_from_db()
    # choices = ["sir", "world",]
    return choices[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:

from artest import autoreg

@autoreg("a5f4cb0f")  # 🎉 add this to auto create test case
def hello(say):
    to = to_whom(1)
    return f"{say} {to}!"

def to_whom(x):
    choices = read_from_db()
    # choices = ["sir", "world",]
    return choices[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 id a5f4cb0f whenever 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 autostub

In 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.

from artest import autoreg, autostub

@autoreg("a5f4cb0f")
def hello(say):
    to = to_whom(1)
    return f"{say} {to}!"

@autostub("35988d25")  # 🎉 add this to auto create stub function
def to_whom(x):
    choices = read_from_db()
    # choices = ["sir", "world",]
    return choices[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 artest

Once artest is installed, you can run it in test mode using the command:

python -m artest

This 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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

py_artest-0.3.0.tar.gz (16.4 kB view details)

Uploaded Source

Built Distribution

py_artest-0.3.0-py3-none-any.whl (18.0 kB view details)

Uploaded Python 3

File details

Details for the file py_artest-0.3.0.tar.gz.

File metadata

  • Download URL: py_artest-0.3.0.tar.gz
  • Upload date:
  • Size: 16.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.7.0 CPython/3.9.15 Darwin/21.6.0

File hashes

Hashes for py_artest-0.3.0.tar.gz
Algorithm Hash digest
SHA256 7c99dcbb816927e9322b84a038b785ec814f033fe740888e9c68465006a571f4
MD5 92d9f578eb799a5ec13a241076697f28
BLAKE2b-256 9c01890a4ae3e03a7ba042c4937c7f913d20c6f4bda60de01dcf66da582aa39e

See more details on using hashes here.

File details

Details for the file py_artest-0.3.0-py3-none-any.whl.

File metadata

  • Download URL: py_artest-0.3.0-py3-none-any.whl
  • Upload date:
  • Size: 18.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.7.0 CPython/3.9.15 Darwin/21.6.0

File hashes

Hashes for py_artest-0.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 39996ff4ca6082d57b0600c82755789b04a0711adbeee705ad8214c0a7c1c6f1
MD5 f50370339f83d619a3897884e201f11f
BLAKE2b-256 b8dbb085f510352ebba1d7a9f123168a48bda6c69ee99456ca4b8b5fc8a4062d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page