We have three stages in which Test Automation evolved.
The First Stage of Old Fashioned Tools
The first stage of test automation services is filled with some best old-fashioned testing tools such as WinRunner, Silk Test, and QTP. These testing tools started it all and set a stage for next level of testing automation innovations like Selenium.
The Second Stage of Selenium
Selenium began the second stage of test automation, focused more on developers and programming best practices when creating automated tests.
But, even then flaky tests and maintenance were driving the testers crazy.
The current buzz all around is Ai and Machine Learning. Companies are rushing to create tools they can pitch as “Ai-driven.”
In fact, at a recent Google conference CEO Sundar Pichai stated that
“We’re moving from a mobile-first to an Ai-first world.”
Below are seven “Ai”-based test automation tools that will take us to the next stage of test automation, the Third Ai Stage.
(Also, check out the Automation Webinar on Ai in test automation)
The Third Stage of Ai in Test Automation
Applitools is one of the first tools in the third stage that made testers start believing that a new way of testing is possible.
The tool uses visual validation testing, and a sophisticated algorithm to find out potential bugs in the application without explicitly calling out all the elements.
One best part about this tool is that no additional visual processing settings, percentages or configurations are required to set up to create visual testing with Applitools.
The algorithm is completely adaptive, and can take the technology as Ai and machine learning advances even further.
The recent version of Applitools, with some cool features adding on top of their existing machine-learning technology.
Sauce Labs is one of the first players in the cloud-based test automation techniques, but with all the data they currently have access to the tool is in a great position to leverage machine learning and come up with some great insights.
Sauce Labs is running over a million and a half tests a day; they have a virtual treasure of data that can be used to help their customers become better testers.
Testim leverages machine learning to speed up the authorization, execution and most importantly the maintenance of automated tests. Testim focuses on reducing flaky tests and test maintenance, which is seen as one of the most significant challenges for most organizations.
Oren Rubin, co-founder of Testim, mentioned in a recent TestTalks interview that the firm’s main goal is to help liberate test automation from the exclusive realm of developers and make it simple enough for anyone on the team to create.
Sealights is a Cloud-based platform. Everyone knows that developers and QA team –are busy these days using Continuous Integration and Continuous Development practices. They have frequent releases and will not have enough time to test the entire application multiple times.
That’s one of the main reasons Sealights was created.
With the machine learning-like technology, it analyzes both code and the tests that run against it, it lets the QA team know exactly what the tests are covering and what they’re not.
But when Sealights says “tests,” they don’t only mean unit tests; they mean any kind of test, from functional, manual, performance, you name it.
“Quality Risks” is even a more exciting insight that the tool provides, as it focuses the user’s efforts on the things that matter, by letting them know exactly which files/methods/lines have changed in the last build that wasn’t tested by a specific test type (or any test type). It can easily ensure that untested code will not reach production before undergoing, at the very least, a minimal validation.
As we move toward CI/CD, dash boarding becomes critical.
If you’re like most companies, everything today is within your CI/CD, but often this data is not visible or accessible for consumption by your teams.
Test.AI is billed as a tool that will add an Ai brain to Selenium and Appium. It was created by Jason Arbon, co-author of How Google Tests Software and the founder of appdiff.
Tests are defined in a simple format similar to the BDD syntax of Cucumber, so it requires no code and no need to mess with element identifiers.
The Ai identifies screens & elements dynamically for any app and automatically drives the application to execute test cases.
The tool is smart enough to know that if an element ever changes, it can adjust and identify manual changes are required. This tool is still in beta.
Mabl is similar to Test.AI.
Mabl started by a bunch of ex-Google employees to run functional tests against the apps or website. In Mabl terminology, the tests are trained to interact with the applications. After recording, the trained tests will run at a predetermined amount of time and alert you.
The tool uses Ai to automatically test the application.
ReTest claims to be different from other test automation tools because it was built specifically for testers.
It also stems from an artificial intelligence research project, so it tries to bake that Ai intelligence into their tool, effectively eliminating the need for their users to possess any programming skills.
Clearly, Ai/Machine Learning is the latest buzz word currently being used in the testing/ test automation industry. But is it real, or just hype?
Only time will decide if the third stage will finally fulfill the promise of reliable, easy-to-maintain test automation for all.
Let me know what your experience has been with these or any other tools you consider to be part of Ai in test automation.
Source URL: https://www.joecolantonio.com/2017/11/07/7-innovative-ai-test-automation-tools-future-third-wave/