Optimizing Testing Using AI and ML

We’ve all heard the phrase: “work smarter, not harder.” Well, this is what today’s testing organizations are focused on, as more companies implement DevOps and Continuous Integration (CI) and Continuous Delivery (CD) models. In fact, according to the 2018-2019 World Quality Report, all but 1% of organizations surveyed are now using DevOps practices. And with new technologies and engagement methods being introduced to the mix daily, ensuring the quality of the apps you deliver has become even more of a challenge. Thankfully, these added complexities may be overcome through machine-based intelligence. With the rapid development and delivery of applications, we have no other choice than to “test smarter, not harder!”

The key to testing smarter is leveraging machine learning to mimic human behavior and perform automated precision-based Continuous Testing (CT). For example, an AI-powered CT platform can recognize changed controls more efficiently than a human, and with constant updates to its algorithms, even the slightest changes may be observed.

Benefits of Using AI in Testing

Increased testing accuracy

Even the most meticulous tester makes mistakes when performing mundane repetitious manual testing. Automated testing ensures the same steps are accurately performed, every time they are executed and never fail to record detailed results. Testers freed from repetitive manual tests have more time to create new automated software tests and deal with complex features.

Helps developers as much as testers

Developers may use shared automated tests to quickly catch problems before going to QA. Tests can run automatically whenever source code changes are checked-in, and notify the team or the developer if they fail. Features like these save developers time and increase their confidence.

Expands your test coverage

Continuous testing using AI expands the depth and scope of tests, thus improving the quality of your apps. For example, automated testing can review memory and file contents, internal program states, and data tables to ensure your app is performing as expected.

Proven AI-based testing automation tools are already being used today. These include Testim.io, which makes use of ML for the authoring, execution and maintenance of automated tests, and Appvance, which uses AI to generate test cases based on user behavior. A huge advantage of these tools is that they become smarter with use, increasing the stability of test suites, and comprehensively covering what actual end users do on production systems.

Ultimately AI, machine learning, and analytics will act as catalysts to true test automation: recommending tests to perform, learning continuously, predicting business impact, and enabling development teams to fix issues before they occur.

The 2018-19 World Quality Report from Capgemini, Sogeti, and Micro Focus cites the increasing adoption of AI as one of the most effective means of improving testing teams.

57% of CIOs and Senior IT Executives report they already have projects involving the use of AI for QA Testing in place or planned for the next 12 months.

While the use of AI in testing is still in the infancy stage, many CIOs and senior IT executives believe AI will enable their organization to transform testing into an end-to-end, self-generating, self-running and self-adapting process.

Though 45% of CIOs and Senior IT Executives stated they were already using AI for intelligent automation testing, a majority of respondents felt automation testing using AI will require new testing strategies and skill sets, such as QA strategists and AI test experts. As with every new technology, it takes time to develop the proper guidelines, standards and approaches. But this isn’t preventing organizations from experimenting with new AI and ML testing approaches.

In the next couple of years, expect to see a widening adoption of AI/ML by more and more organizations. Yes, failures will arise, but we will learn from them until we ultimately figure how to leverage the new technologies to achieve the best results. The biggest current hurdle for many is achieving the automation-maturity level needed before they can start to think about AI and ML in their testing organizations.

If you haven’t given AI-based automated testing much thought, now is a great time to start. Many industry-leading companies have successfully implemented and deployed AI by first developing strategies to leverage these emerging technologies, and ensure they were aligned with business drivers. To learn more about how Anexinet can help you define and develop your Automation Testing Strategy leveraging AI and ML (or any other enterprise ML strategy), check out our .