A few decades back, Artificial Intelligence (AI) was no more than a dream, but now it has become such an inseparable part of our daily routine that imagining life without AI seems impossible. From self-driving cars, smartphones, and smart devices like Alexa and Siri to Google Maps that help you navigate your daily life, everything has become influenced or driven by AI. Thanks to the technology revolutions that make impossible things possible!
What is Artificial Intelligence?
Lets first understand what does artificial intelligence mean. Forrester defines AI as
“A system, built through coding, business rules, and increasingly self-learning capabilities, that is able to supplement human cognition and activities and interacts with humans natural, but also understands the environment, solves human problems and performs human tasks.”
In simple words, AI enables machines to learn through data giving them the capability to make a decision. The algorithms are not written to solve a particular problem rather they are designed in such a way to enable the system to make a decision based on data.
AI and Machine Learning (ML) go hand in hand
Since the 1950s, AI has been experimented within different fields to find its ground and the term that finally made its way as an important part of AI is machine learning. ML or Machine Learning is an important part of AI that uses pattern recognition technology.
A very common and explainable example of prediction using ML is searching for something on Google, Amazon or your favorite YouTube channel. Here the ML algorithm reads and saves the queries made by you and the next time you search for something; it suggests the queries based on your past searches. It’s like these algorithms try to read your mind and guess what you may ask for. Isn’t it amazing? Yes, it is, and that’s why AI is so powerful these days.
Influence of AI in Test Automation
- Reducing Maintenance and Eliminating Flaky Tests
One of the most common problems with test automation is maintenance. For example, say we have 100 automated tests running daily to ensure the main functionalities of the application are still stable; What if the next day we come back to work and find that half of the tests have failed? We would need to spend considerable amounts of time to troubleshoot the failures and investigate what happened. This involves figuring out ways to fix the failures and implement the fixes. Then, we re-run the automated tests to ensure everything passes.
AI can avoid issues like this due to its self-healing mechanism. It can start detecting problems in the tests before they even occur, thus proactively fixing tests instead of us reacting to them. Based on the number of times the tests have run, the AI can figure out which tests are stable or flaky. As a result, it can give us data on what tests need to be modified to ensure test runs are stable.
Finally, based on large numbers of test runs, AI can optimize the wait times used in tests to wait for the pages to load and also can handle tests running on different resolutions. All of this adds up to a considerable decrease in the time spent on the maintenance of tests and helps to solve one of the biggest bottlenecks of continuous testing.
- Automatically Writing Test Cases
The biggest application of ML/AI in test automation has been in automatically writing test cases for software. AI/ML tools have gone far ahead to learn the business usage scenarios of the application under test. They just need to be pointed to the software. While learning the application, they automatically crawl and collect useful data like screenshots, HTML pages and page loading time. Over time they collect enough data from the application so that they can train the ML model for expected patterns of the app.
When they are run/ executed, the current state of the app is compared with the known or saved patterns. If there is any error, visual difference, slow run time or similar issue, then the system automatically marks it as a potential issue. However, in some cases, the differences might be valid. In that case, the tester needs to validate the bug or issue.
- Removing Dependencies
Another challenge of test automation is writing tests for a system that may have dependencies on other modules that may or may not have been implemented yet. Now AI can help to do this for us.
Once we have authored some tests and have run them consistently for some time, the AI can start recording all the server responses. The next time we run the tests, instead of talking to a server or database, the test will access the stored responses (which was facilitated with the help of AI) and will continue to run without any obstacles.
As a result, the tests run much faster, since the delay in waiting for a response is eliminated and the need to rely on a physical database or server has completely been erased.
How AI- Testing Automation Transforms Business
Businesses that commit to implementing AI at the enterprise level are already experiencing greater operational efficiency and better product results. Developers are renegotiating their involvement within Agile and DevOps strategies, as smart algorithms are now capable of tackling the most repetitive problems presented in testing automation. The function is partnering with Google Cloud to build advanced anomaly detection through canary testing where a small set of users are used for real-world testing of new code. AI is used to compare the experience of these users with those running the existing code. Anomalies can then be identified automatically, and details passed back to the developers.
Top Automation Testing Tools
Following are some popular Automation Testing Tools.
Selenium is the household name when it comes to testing automation. It is considered the industry standard for user interface automation testing of Web applications. Almost nine out of ten testers are using or have ever used Selenium in their projects, according to Survey on Test Automation Challenges.
Selenium has recently released its first alpha version of Selenium in April 2019. The announcement of Selenium 4 official version is yet to be determined, but you can expect the release would come with many improved and enriched features.
- Website: https://www.seleniumhq.org/
- License: Open-source
- Katalon Studio
Katalon Studio is a powerful and comprehensive automation solution for testing API, Web, and Mobile apps. It has a rich feature set for these types of testing and supports multiple platforms including Windows, macOS, and Linux.
Katalon Studio was named a March 2019 Gartner Peer Insights Customers’ Choice for Software Test Automation with more than 450 positive reviews, which once again, shows the tool is now one of the biggest players in the market.
- License: Free
UFT is a popular commercial tool to test desktop, Web, and mobile applications. It has been extended to include a good set of capabilities for API testing. By supporting multiple platforms for the target application under test (AUT), UFT provides a convenient choice to test the AUT that operates on desktop, Web, and mobile.
UFT provides several advanced capabilities for smart object detection, image-based object detection, and correction. In May 2019, Microfocus has released the latest version of UFT (v14.52) with new features and enhancements
- License: From $3,200 per year
Testers can easily use TestComplete’s record and playback feature, like Katalon Studio. They can insert checkpoints into test steps to verify results. As a product of SmartBear, TestComplete can be integrated easily with other products offered by SmartBear.
- Website: https://smartbear.com/product/testcomplete/overview/
- License: From $2,399 per year
Watir is an open-source testing tool for web automation testing based on the Ruby library. Watir supports browsers including Firefox, Opera, headless Browser, and IE. It also supports data-driven testing and integrates with BBD tools such as Rspec, Cucumber, Test / Unit.
- Website: http://watir.com/
- License: Open-source