Unit testing is key to developing quality code. There’s a host of libraries and services available that you can use to perfect testing of your Python code. However, “traditional” unit testing is time intensive and is unlikely to cover the full spectrum of cases that your code is supposed to be able to handle. In this post, I’ll show you how to use property-based testing with Hypothesis to automate testing of your Python code. I also discuss some of the advantages of using a property-based testing framework.
Unit testing involves testing individual components of your code. A typical unit test takes input data, runs it through a piece of code, and checks the result against some pre-defined expected outcome.
Hypothesis does something different. It is a property-based (or: “generative”) testing framework. A property-based test involves defining general expectations about your code instead of specific examples. For example, if you have some code to calculate total VAT on a number of transactions, you could define a bunch of hypothetical numbers with their corresponding VAT amount ($100 transaction → $xx.xx tax) and put this to the test. However, if you know that VAT is, let’s say, 20 percent, a property-based test would verify that total VAT always is 20 percent of the total amount.
Hypothesis is built on these principles. It _generates arbitrary input data according to some specification _and subsequently puts that data to the test. What’s more, when Hypothesis finds an example that causes an assert failure, it’ll try to simplify the example and find the smallest failing case — a process called “shrinking”. Hypothesis will essentially try to “break” your code. Your tests will therefore cover a much larger chunk of your domain space with the same amount of code. And, you’re bound to find edge cases that you hadn’t even thought of.
Let’s see how Hypothesis works in practice. Hypothesis has three key components: the code that you’re testing, the strategies that define your test data, and a function that tests your code using the strategies.
Let’s assume we have a simple (and non-sensical) piece of Python code that converts a float value to an integer:
This code has a clear property: the outcome should always be an integer type.
To test this code, we’ll first define a “strategy”. A strategy defines the data that Hypothesis generates for testing, and how examples are “simplified”. In our code, we only define the parameters of the data; the simplification (or: “shrinking”) is internal to Hypothesis.
We’ll start with a strategy that generates a float value between 0.0 and 10.0 (inclusive). We define this in a separate file called
data_strategies.py. Using a dataclass for this may seem like overdoing it, but it is useful when you’re working with more complex code that takes a bunch of different parameters.
Using a dataclass and Hypothesis to define your data strategies
A lot of time can go into defining the strategies, and in fact, it should. The whole point of property-based testing with Hypothesis is that you define the parameters from which data is generated, so that you subsequently allow your automated testing to do its magic. The more time you spend on this, the better your testing is bound to be (think: “high investment; high reward”).
#python3 #data-science #coding #python #programming
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners
The demand for delivering quality software faster — or “Quality at Speed” — requires organizations to search for solutions in Agile, continuous integration (CI), and DevOps methodologies. Test automation is an essential part of these aspects. The latest World Quality Report 2018–2019 suggests that test automation is the biggest bottleneck to deliver “Quality at Speed,” as it is an enabler of successful Agile and DevOps adoption.
Test automation cannot be realized without good tools; as they determine how automation is performed and whether the benefits of automation can be delivered. Test automation tools is a crucial component in the DevOps toolchain. The current test automation trends have increased in applying artificial intelligence and machine learning (AI/ML) to offer advanced capabilities for test optimization, intelligent test generation, execution, and reporting. It will be worthwhile to understand which tools are best poised to take advantage of these trends.****
#automation-testing #automation-testing-tools #testing #testing-tools #selenium #open-source #test-automation #automated-testing
Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map
What do you do when you have lots of free time on your hands? Why not learn test programming strategies and approaches?
When you’re looking for places to learn test programming, Test Automation University has you covered. From API testing through visual validation, you can hone your skills and learn new approaches on TAU.
We introduced five new TAU courses from April through June, and each of them can help you expand your knowledge, learn a new approach, and improve your craft as a test automation engineer. They are:
These courses add to the other three courses we introduced in January through March 2020:
Each of these courses can give you a new set of skills.
Let’s look at each in a little detail.
“Appium is an open-source tool for automating native, web, and hybrid applications on different platforms.”
In the introduction, Orane describes the course parts:
The first chapter, broken into five parts, gets your system ready for the rest of the course. You’ll download and install a Java Developer Kit, a stable version of Node.js, Android Studio and Emulator (for a mobile device emulator), Visual Studio Code for an IDE, Appium Server, and a sample Appium Android Package Kit. If you get into trouble, you can use the Test Automation University Slack channel to get help from Orane. Each subchapter contains the links to get to the proper software. Finally, Orane has you customize your configuration for the course project.
Chapter 2 deals with elements and screen interactions for your app. You can find elements on the page, interact with those elements, and scroll the page to make other elements visible. Orane breaks the chapter into three distinct subchapters so you can become competent with each part of finding, scrolling, and interacting with the app. The quiz comes at the end of the third subchapter.
The remaining chapters each deal with specific bullets listed above: sessions and screen capture, timing, element attributes, and using element states. The final summary chapter ensures you have internalized the key takeaways from the course. Each of these chapters includes its quiz.
When you complete this course successfully, you will have both a certificate of completion and the code infrastructure available on your system to start testing mobile apps using Appium.
Andrew Knight, who blogs as The Automation Panda, teaches the course on Selenium WebDriver with Python. As Andrew points out, Python has become a popular language for test automation. If you don’t know Python at all, he points you to Jess Ingrassellino’s great course, Python for Test Programming, also on Test Automation University.
In the first chapter, Andrew has you write your first test. Not in Python, but Gherkin. If you have never used Gherkin syntax, it helps you structure your tests in pseudocode that you can translate into any language of your choice. Andrew points out that it’s important to write your test steps before you write test code — and Gherkin makes this process straightforward.
The second chapter goes through setting up a pytest, the test framework Andrew uses. He assumes you already have Python 3.8 installed. Depending on your machine, you may need to do some work (Macs come with Python 2.7.16 installed, which is old and won’t work. Andrew also goes through the pip package manager to install pipenv. He gives you a GitHub link to his test code for the project. And, finally, he creates a test using the Gherkin codes as comments to show you how a test runs in pytest.
In the third chapter, you set up Selenium Webdriver to work with specific browsers, then create your test fixture in the pytest. Andrew reminds you to download the appropriate browser driver for the browser you want to test — for example, chromedriver to drive Chrome and geckodriver to drive Firefox. Once you use pipenv to install Selenium, you begin your test fixture. One thing to remember is to call an explicit quit for your webdriver after a test.
Chapter 4 goes through page objects, and how you abstract page object details to simplify your test structure. Chapter 5 goes through element locator structures and how to use these in Python. And, in Chapter 6, Andrew goes through some common webdriver calls and how to use them in your tests. These first six chapters cover the basics of testing with Python and Selenium.
Now that you have the basics down, the final three chapters review some advanced ideas: testing with multiple browsers, handling race conditions, and running your tests in parallel. This course gives you specific skills around Python and Selenium on top of what you can get from the Python for Test Programming course.
#tutorial #performance #testing #automation #test automation #automated testing #visual testing #visual testing best practices #testing tutorial
Recently, after writing a microservice to manage Create-Read-Update-Delete (CRUD) operations for a MongoDB database, it logically was time to write unit test cases. As I had developed the microservice using Python and Flask, the unit test also had to be in Python. For unit testing, I used the unittest package.
Writing Unit Tests
By writing unit tests, not only was I ensuring that I could run the same tests over and over again but, assuming that the underlying functionality of the micro-service had not broken, I could expect the same results for each execution. Moreover, as the number of REST functions increased, the unit tests would help me ensure that none of the existing functionality was broken. Additionally, the unit tests would help me reduce the total amount of time I needed to spend on testing the same functionality over and over again.
#python #python development #python unit test #python unit testing