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AI Test Automation: Addressing the Challenges of Traditional Testing Approaches

April 27, 2022
   /   
by 
Sohaib Zaidi

Artificial intelligence has transformed nearly every industry, and testing is no exception. While many enterprises have embraced open-source test automation frameworks to support agile methods of development, these traditional test automation tools fail miserably when it comes to keeping pace with the rapid release cycle of ERP vendors.

In a world where Oracle rolls out updates 4 times a year, Salesforce thrice a year, and SAP, Workday and Dynamics 365 twice a year, traditional test automation tools like Selenium struggle due to their inability to quickly create test cases, and failure to easily maintain test scripts. The answer to these challenges is AI test automation.

In this post, we’ll discuss the short comings of traditional test automation, and will explain how leveraging AI can simplify and improve the software testing process. We’ll highlight how AI test automation speeds up test creation, minimize maintenance efforts, and enables non-technical users to get involved with test automation.

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Challenges with traditional test automation

  • Coding knowledge is a prerequisite

Most test automation tools, like Selenium, require coding skills. While these work for technically-trained employees, most business users simply cannot use these tools without months of training. In practice, these tools require the tight collaboration of business users—who know their business processes best—in addition to technical resources who know how to code. This means that more people, and more time, is required to manage an automated testing program.

  • Maintaining test scripts

Most traditional test automation frameworks rely on fixed element properties like ID, Name, Class, Xpath, Tag Name, and CSS selector to identify controls.  This is problematic because most packaged applications have dynamic properties, meaning that a slight UI or back-end change will lead to test failures that are extremely time consuming to fix.

Additionally, code-based test automation becomes unmaintainable as it grows over time. When a new member joins the team, they must learn about the scripts from the coders who wrote them—a long and difficult process. Scripts cannot easily be duplicated or shared, meaning that organizations are overly reliant on the people who wrote them.

  • Not knowing what to test

Even though traditional test automation tools increase the efficiency of software testing, there’s no guarantee that the tool provides adequate test coverage. Typically, business users and IT teams need prolonged meetings to outline their business processes, and create tests based on personal experience. But as you can imagine, this is largely guesswork.

As a result, most organizations either test too much—wasting precious time and money—or test too little, which exposes their applications to downtime risk.

How AI test automation addresses these challenges?

AI test automation solves the 3 issues outlined above, through:

  • Natural language processing

Natural Language Processing (NLP), a subset of AI, makes coding unnecessary for test script creation.  Using NLP in test automation enables employees to write tests in plain English, greatly reducing the learning curve. As tests are written in natural language, it is not mandatory for a tester to have programming knowledge. Thus, no-code test automation saves your employees months of ramp-up.

Codeless automation tools also enable business users to create and scale test automation without the need for IT, greatly reducing the cost of test automation.

Read how Opkey's No-code Test Automation Platform enabled Boston Consulting Group to Update their Oracle EBS to RPC5 with minimal effort, time and money.

Self-healing test scripts: An AI test automation tool automatically identifies changes made to object properties, such as Name, ID, Xpath, CSS, etc. that could lead to flaky/brittle tests. With self-healing capabilities, changes get fixed automatically without human intervention. By largely reducing test maintenance efforts, your QA teams can focus on more high value activities.

Test mining:  By utilizing AI-driven test mining, organizations can understand the exact business processes of their employees, and then prioritize testing on the most critical ones. Said another way, AI ensures that your continuous testing program has adequate test coverage. 

Wrapping up

Incorporating artificial intelligence in testing solves three core issues of traditional test automation: a steep learning curve, burdensome test maintenance, and not knowing what to test.

Opkey is the industry’s leading AI test automation tool, that’s helped more than 250+ global enterprises streamline their packaged application testing.

Get in touch with us today.

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