As a technical search-engine optimizer, I’m often called in to coordinate website migrations, new site launches, analytics implementations, and other areas that affect sites’ online visibility and measurement to limit risk. Many companies generate a substantial portion of monthly recurring revenue from users finding their products and services through search engines. Although search engines have gotten good at handling poorly formatted code, things can still go wrong in development that adversely affects how search engines index and display pages for users.
I’ve been part of manual processes attempting to mitigate this risk by reviewing staged changes for search engine optimization (SEO)-breaking problems. My team’s findings determine whether the project gets the green light (or not) to launch. But this process is often inefficient, can be applied to only a limited number of pages, and has a high likelihood of human error.
The industry has long sought a usable and trustworthy way to automate this process while still giving developers and search-engine optimizers a meaningful say in what must be tested. This is important because these groups often have competing priorities in development sprints, with search-engine optimizers pushing for changes and developers needing to control regressions and unexpected experiences.
Many websites I work with have tens of thousands of pages. Some have millions. It’s daunting to understand how a development change might affect so many pages. In the world of SEO, you can see large, sitewide changes in how Google and other search engines show your pages from very minor and seemingly innocuous changes. It’s imperative to have processes in place that catch these types of errors before they make it to production.
Below are a few examples of problems that I have seen in the last year.
A proprietary third-party SEO monitoring tool we use, ContentKing, found this problem immediately after launch to production. This is a sneaky error because it’s not visible in the HTML, rather it is hidden from view in the server response header, yet it can very quickly cause the loss of your search visibility.
#automate testing #python #python tool
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
I took Nyran Moodie’s course on Test Automation University: Automated Visual Testing with WebdriverIO.
If you use WebdriverIO regularly, and you are unfamiliar with the basics of using Applitools for automated visual testing, you will appreciate this course. Nyran focuses you on how to add visual test automation with Applitools to your WebdriverIO tests.
Nyran expects you to know WebdriverIO. If you want to learn WebdriverIO, check out Julia Pottinger’s excellent course — UI Automation with WebdriverIO on Test Automation University.
Nyran breaks the course into eight main chapters. He gives this description in the course overview:
Each of these chapters provides a methodical approach to getting going with Applitools. I’ll give a quick overview of each.
If you read this review or take the course, you know why visual testing. Your UI and end-to-end tests result in the rendered output. You can write all the functional tests that grab locators, enter data, effect action, and cause the appropriate output locators to have the appropriate values. But, until you look at the result, you cannot tell if the input and output conform to design and usability expectations.
Nyran did not explain the most frequently-experienced reason for visual testing — unintended consequences of code changes over time. Our experience shows us that most expected application changes get tested, but unintended changes cause problems.
Nyran does a nice job explaining how to get started. You need an Applitools API key, which you can get from the Applitools console. Nyran explains why you set up a local environment variable for your API key (so you do not need to include your API key in your test code directly). He also points to the GitHub repo he uses for all the examples in the course.
Chapter 3 involves the first coding examples for setting up Applitools. With a simple:
npm install @applitools/eyes.webdriverio
You get the the node instructions for installing the Applitools Eyes to your WebdriverIO setup. After this, you can install the Applitools Eyes service to your tests. He shows code examples of what test code looks like when calling Applitools:
#performance #testing #automation #automated testing #webdriver #visual testing #automated testing best practices #webdriver io #ui automation tools
In recent years, the testing community has witnessed a steady rise in automation testing. Although it has proved significant advantages compared to manual testing, flakiness in locating objects for WebUI testing is still a constant problem that restrains the transition to automated testing for teams. Testers have to spend unnecessary time and effort to manually update the object locators whenever they make new changes to the application.
The Self-healing mechanism is one of the most promising factors to completely change the automation Web testing. By eliminating unnecessary repetitive tasks in execution, this mechanism creates the foundation for test automation to perform at its full potential.
Object locator flakiness comes from the root of automation itself: fragile test scripts. Object locators, most often, can no longer identify the target element after the application under test (AUT) changes. Pre-written scripts then will fail to run the test correctly, which results in a false failed result. The QA team has to detect whether their tests fail because they actually have a problem, or the scripts cannot adapt to new element changes. Web testing, in which UI changes happen over and over, appears to suffer the most from this flakiness.
To maximize the speed advantage of automation testing, automation testers demand a mechanism that can recognize and skip these UI changes to keep the scripts running seamlessly. For automation testers, Self-healing function has turned out to be the optimal solution for this pain point.
The human body is a perfect example to explain the self-healing mechanism. Whenever a human gets injured, the human self-healing function is triggered to find the wound and heal it without any action needed. For any minor injuries, this mechanism can quickly heal wounds without human intervention.
Back to self-healing automation in software testing, it functions exactly like the way the human body works. Based on the object changes in properties, the self-healing mechanism actively studies the characteristics and properties of all available objects, removing similar objects on the page and comparing their historical data. Then, the test engine will automatically pick up an alternative that best assembles the changed locator to keep the test running.
Self-healing has been a desirable solution to solve one major challenge of automation testing: Quality at speed.
By identifying new changes and replacing them with similar objects, the self-healing mechanism reduces downtime and significantly speeds up the deployment process. With this capability, QA teams can release the burden of intense script maintenance, and save time and resources.
Additionally, a well-built self-healing mechanism gives QA teams the ability to create generic test frameworks that can apply to a broad range of software, improving the benefits of automation.
More and more automation tools have developed self-healing as an essential part of the tool in response to this enormous demand. Many experts expect that self-healing will become less of a nice-to-have function but a must-have for all automation tools in the near future.
In their latest update — version 7.6 — Katalon Studio company has introduced the new self-healing mechanism to its users. This function allows users to speed up processes and maximize automation efforts for the whole team.
See what’s new in Katalon Studio.
Before the updated version was released, Katalon Studio users were familiar with its Auto-healing Smart XPath. This plugin assists in recovering broken default locators by trying other available XPath alternatives. However, in case there are no suitable alternatives available, the execution will stop running.
To advance its process and provide users with more efficient test maintenance, Katalon Studio developed the Self-healing mechanism that has been released lately. Following the positive impacts of Smart XPath, the self-healing mechanism is made to be more robust and comprehensive to tackle broken locators during each execution.
During the execution, if Katalon Studio fails to identify an object with its default locator, the tool tries other pre-configured locators associated with that object. With 4 selection methods, including XPath, Attributes CSS, and image, users can opt to set the object locating priority to ensure accuracy and continuity of the execution.
If Katalon finds a suitable object within the selectors, the test keeps running to completion. With approval from the tester, the locator that is selected to replace the broken object will then be saved for that particular Test Object in future executions. By doing so, Katalon Studio creates a database to make the self-healing mechanism even more efficient by avoiding analyzing one same broken object repeatedly.
Once the execution is over, Katalon Studio will suggest replacing all broken locators with the locators having found the objects. If Katalon Studio cannot find the target objects, the test will either continue or stop running, depending on how users designed the failure handling option.
In a Katalon Studio project, you can find a screen in project settings dedicated to Self-healing. You can change the default settings to make the utility better suit your needs.
The self-healing mode is activated by default whenever users run the application. To open the Self-healing window, go to Project/Settings/Self-Healing/Web UI.
To start with Katalon Studio’s self-healing, users need to set initial settings to decide the default selection method used during spying and recording. Please note that for Image selection, screenshots for the objects are mandatory.
Go to Project/Settings/Self-healing/Web UI/Test Design to set the default selection method.
Users can decide which alternative locators to be used in terms of selection methods and their priorities in Project/Settings/Self-Healing/Web UI/ Test Execution.
This predefined order will be applied when Katalon fails to locate the target objects.
All the replacements are recorded in Self-healing Insights when the execution is over. Katalon Studio allows users to decide which alternative locators to be used in terms of selection methods and their priorities.
To read the full tutorial and sample project using the self-healing mechanism, you can visit Katalon’s Document hub here.
With this upgraded Self-healing mechanism, Web testing is made faster and easier as it reduces human efforts on script maintenance with less interruption. This is a valuable addition for Katalon users to have a more powerful automation experience with consistent results.
To try out this new functionality, sign up on the Katalon website with your business email and get a 30-day free trial with full functions in the latest Katalon Studio!
#devops #software testing #automated testing #automation testing #update #software test automation #xpath #automation tools #software testing 2020 #object locator
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
Admin Panel Finder
Advance Dork Finder
Hash Crack (Online-Database)
Hash Crack (Wordlist)
Tcp Port Scan
Geo IP Lookup
Reserve Analysts Search
Csrf Vernavility Checker
WordPress Username Finder
#testing #advance web penetration testing tool for python #python #advance web penetration #testing tool for python #web