Transforming automated testing in the cloud with AI and ML


While the artificial intelligence (AI)/machine learning (ML) phenomenon has been around for quite some time now, it was only in 2020 that digital transformation, and an overall shift to the cloud, became imperative for organizations of all sizes and in all sectors. Also, with the remote economy steadily rising, the focus on high-quality, readily scalable applications on the cloud has taken center stage on the average CTO’s agenda.

According to Gartner, by 2022, at least 40% of new application development projects will have an AI-powered virtual developer on their team, primarily because AI and ML have immediate potential in terms of quality assurance and application testing. At the heart of application performance, of course, is quality assurance. Regardless of whether you’re building net-new applications, migrating to the cloud, or modernizing your existing application stack, every release needs quality assurance and its most important component—application testing.

Embracing AI/ML for application testing: The what and the how

The need for leveraging AI/ML for cloud-based testing emanates from several factors, including analysis of large amounts of test data (log files) and application performance analysis. One of the key drivers of test automation has been the rise of DevOps and microservices, whose very nature requires incorporating intelligent automation in the Software Development Life Cycle (SDLC) as much as possible.

Another important component pushing AI in application testing is the continuously evolving customer experience—a must-have for business growth. Combining these factors, the future of application testing has to be rapid, accurate, continuous, and not resource-intensive, which is everything the traditional manual testing approach is not.

Use cases

Following are some AI/ML use cases:

Visual UI testing

Visual UI testing is one of the most common use cases of how businesses use AI/ML currently. By using ML to depict patterns and create visual validation tools, you can test UI elements of any application for appearance and functionality and ensure that the UI looks overall right to the user as planned.

Application crawling

Another popular test automation use case is the usage of application crawling. In this scenario, you direct the ML model to run the application. As it progresses, it generates test scripts, runs them, and validates them against previous states continuously, flagging any deviations or failures within the application.

API testing

API testing is another common use case for AI/ML in testing. Machine Learning models can generate multiple test scenarios based on the API functionalities and generate consistent test data on the performance of the API, which you can then store in a central developer knowledge base.

Intelligent test automation in the cloud

As the scope of application quality assurance and intelligent testing automation increases across the enterprise, the amount of data and testing scripts generated grows exponentially. Centralized access for developers requires the proper infrastructure, with hybrid clouds the currently preferred model. However, for some enterprises, especially in the SMB segment, hyperscalers offer more integrated AI/ML tools and are the preferred choice.

Resources and expertise

According to the Tech Beacon World Quality Report, 88% of enterprises consider testing with AI and testing of AI as the strongest growth portion of their test-automation efforts, while 80% plan to put more AI trials/proofs-of-concept in place.

Organizations that quickly switch to the DevTestOps model often face a shortage of the talent and expertise necessary to accurately implement their AI/ML models across different use cases and functional areas of the enterprise. This challenge is also often coupled with significant pressure on their budgets. In such circumstances, most companies turn to service partners with the experience and expertise to implement their AI/ML-driven test initiatives at scale and speed.

Rackspace Technology Application Testing

Rackspace Technology Application Testing Services span functional, user acceptance, security, and performance testing for applications, resulting in 80% test-cycle time and test-automation rates greater than 90%. The breadth of the service accelerates applications' movement into production at significantly lower costs. Rackspace provides the right-sized, cloud-first, agile testing-automation capabilities that accelerate your release cycle times, reduce your test automation costs, and improve application quality. Let us help you with your AI/ML challenge so you can reap the benefits.

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Prasanna Lakshminarasimha

Prasanna heads the Applications & Data Delivery Department in Rackspace EMEA. He has more than two decades of experience working across Digital Strategy, Consulting, Transformation, Delivery, Enterprise Architecture, Presales, Business Development, Offering Development, and M&A. His current focus areas include Cloud Native Development and Test Automation.

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