Artificial intelligence (AI) today is making machines learn, adapt, and adjust to new experiences, and perform human-like tasks. It has helped in bringing out certain abilities, which otherwise was very complex to achieve by traditional methodologies. The technology has transformative potential and is now being leveraged in software testing/test automation to overcome challenges and optimize efficiency.

First, let us know some of the common day-to-day test automation challenges that we face.

Skillset: Most open source/off-the-shelf test automation tools require basic to moderate programming skills. Unfortunately, not every tester is trained/skilled enough to develop an automation test suite.

Maintenance: It is a continuous effort to enhance/upgrade the automation test suite with regular product updates/new features. Even the best-designed test automation frameworks require a certain amount of test maintenance throughout their life cycle.

Reporting: Test reporting is one of the important features of a test automation framework. Not all the test automation tools (most open-source test automation tools) provide test reporting by default. Even if they do, historical data and other reporting insights may not be available. This limitation in reporting needs custom programming or support from third-party plug-ins to build additional reporting features

Scalability: As the test automation suite grows over a period, the automation framework should support running tests at scale and provide the test outcome in a matter of minutes, if not hours.

With plenty of AI-powered platforms available, using AI in software testing has become imperative. With advancement in technology, it has become easier to implement artificial intelligence in automation testing. Here are a few test automation tools, which use AI to overcome some of the common automation challenges:

1. 1. Test Project
   2. TestCraft
   3. Applitools
   4. Mabl
   5. Testim

These tools possess certain abilities that help in resolving the above-mentioned challenges. Now let us look in detail at how they work.

1. Auto Maintenance

How does it work with AI tools?

• Automation tools with built-in AI have a different approach for identifying an object.
  Some of these tools use multiple attributes of an element to locate it on the web page instead of a single attribute. By following this approach, even if one attribute changes, the element can still be successfully located with the help of other attributes that have already been extracted from the DOM by the AI.
• This ability saves a lot of test maintenance effort and a test automation engineer can focus on building more automated test scripts or take care of the maintenance for those tests, which cannot be handled by the AI tools.

Example:

• Consider a Login page that has ‘Login Button’ text as ‘Login’. The AI tools will identify this button with various locator strategies Ex: Xpath, Text Based, CSS, href, and index, etc. If the developer changes the text as Sign In, the AI tool will choose other locator types or adapt this change based on its intelligence and identify the locator while test execution.
• AI-based tools like Testim, Mabl, and Test Project supports dynamic locators to make tests more resilient to change.

2. Less or No Coding

How does it work with AI tools?

With a built-in automation framework and smart test recorders, testers can now record the test steps and playback. This does not require any programming abilities. Smart test recorder works differently than standard record and playback tools and has the following additional capabilities:

• Element Explorer: Identifies multiple locator strategies and optimizes for tests.
• Automatic Step Recorder: Interact with the application and the tools record your every single move to create test cases.
• Test Debug and Replay: Easily debug step by step, get visibility to parameters, and identify failed tests with detailed error messages, update test steps, locators, and assertions.
• Export to Code: Export the recordings to Selenium Code.

Tools like Testim, Test project, and Test Craft can be used to develop automation scripts with less or no coding by using inbuilt smart test recorders

3. Visual Testing

Automated visual testing deploys software to automate the process of comparing visual elements across various screen combinations and uncover any visual defects. This approach is very handy to conduct any visual usability testing or to automate certain parts of the system, where the test automation tool in use cannot automate due to technical limitations or usage of third-party controls in the application e.g. graphs, grid tables, reports, etc.

Traditional automation tools are not good at visual testing as pixel variations across screens vary in size, colour, shape, and format.

How does it work with AI tools?

• Applitools, an AI-based visual UI automation tool, determines the element relative location, boundary, and properties (colours, contents, etc.) of each visual element. If no prior baseline is set, these elements are saved as the baseline. Once a baseline is established, Applitools will check for the checkpoint image against the set baseline.
• This modern approach to UI testing reduces code and locators and enables a user to create and maintain tests faster than ever before.
• Applitools eyes work with many existing test automation frameworks, including Selenium, Appium, WebdriverIO, Microfocus (formerly HP), and Unified Functional Testing (formerly QuickTest Professional).

4. Reporting

How does it work with AI tools?

• The AI tool offers indicators of about the product’s health by measuring successful vs. failed tests during various period.
• By using AI tools, the test automation trends can be compared across various browsers, platforms, and devices.
• AI tools provide screenshots of failure/error messages with historical reports without any extra configuration.
• Test Project and Testim tools support these features.

5. Scalability

How does it work with AI tools?

• AI tools come with a pre-setup to execute the tests across various environments, against multiple browsers.
• Execution jobs can be configured and saved to run tests more often and parallel test execution can be achieved easily.
• Certain AI automation tools such as Test Project, Mabl, and Testim allows integration of test automation with their DevOps Pipeline, which helps teams to get their build testing feedback faster.

Most of the AI-based automation tools today are making a positive impact by providing great relief from traditional test automation challenges. With the additional capabilities of these tools, it would only make them go-to tools for test automation teams eventually. So, if you are a leading IT company or involved in testing, AI can open a new dimension for you in the world of test automation.

Contact us to connect with our test automation experts and know more about how ZenQ applies AI technologies for its clients to track changes, fix tests, and recommend suggestions on a test failure.

By: Venkata Sai Annam & Jyothi Muchuram