Two Is Better Than One: How To Combine AI and Automation to Create a Powerful Quality Engineering Process
This article will teach you about incorporating AI and automation techniques to enhance your quality engineering process.
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Over the past decade, the rapid pace of innovation has created a seemingly ever-evolving digital landscape. Due to this changing technological space, organizations are eager to implement tools and adopt platforms and applications that allow them to operate more efficiently and serve customers more effectively. For instance, AI has become increasingly popular as a way to optimize and enhance processes, better differentiate and compete in the marketplace, engage with customers, build new business models, and more. While AI is an excellent tool to bolster almost any operation, combining it with automation can create even more efficient processes. This super-tool is called AI-assisted automation.
For Quality Engineering, in particular, three forms of AI have already proved to be very useful when layered with automation: visual AI, self-healing AI, and risk AI. When these types of AI are combined with automation, Quality Engineering can be enhanced, and companies can become more efficient.
A Breakdown of Visual, Self-Healing, and Risk AI
Visual AI is a form of AI that detects critical changes in what's presented to the user and checks that the application has been rendered correctly. Essentially, visual AI is a digitized version of the human eye, monitoring for readability, usability, accessibility, and more.
When combined with automation, visual AI can identify elements of the user interface on the screen and then automate individual elements. For example, visual AI can locate any button on a user's screen – like a login button – and then be paired with automation to click said button. Additionally, by combining visual AI and automation, technological barriers are removed, and complicated operating systems and languages become easier to navigate. Further, developers can automate applications they may not have full access to.
Self-healing AI, on the other hand, helps adapt test cases and adjust test plans as applications evolve, ensuring tests and codes don't break during new iterations. Self-healing AI can understand the test context and work around new configurations, screens, and input fields without user intervention. Further, it can deal with localized content and interfaces much more efficiently.
Combining self-healing AI and automation can help ensure that entire application suites don't break when small changes are made. For example, when a change is made to a user interface, it can impact various aspects of the application. With self-healing AI and automation working together, AI can locate any issues, and automation can be programmed to fix them. Ultimately, together, self-healing AI and automation ensure that the least amount of manual effort is required to fix problems.
Lastly, risk AI analyzes change and usage logs to help developers detect at-risk objects in an application and perform an impact analysis, selecting the right set of tests focused on the business and technical risk of the changes. This allows quality engineers to be more efficient in their processes, especially when larger regression suites begin taking a long time to run. When combined with automation, risk AI prioritizes where to start looking for issues, and automation with self-healing AI can help fix test suites with the least amount of manual effort possible.
AI-Assisted Automation's Impact on Quality Engineering
AI-assisted automation is holistically helpful for quality engineering. Together, AI and automation allow companies to gain visibility into testing across teams and throughout the software development lifecycle, ensuring issues are resolved quickly and fostering end-to-end quality.
While it can be used together almost everywhere in the software development lifecycle, it's most useful in areas that require or deal with a lot of data, patterns, trends, and anomaly detections.
For example, within quality engineering, visual AI testing can validate the readability of text elements in the background as automation proceeds. When elements overlap, are drawn off-screen, are hidden by images, or contain color combinations that are difficult for color blindness, they typically won't raise errors and will work for most automation. However, visual AI will detect the problem and raise a flag for developers to fix.
Further, when customers make changes to code, security patches, or third-party libraries, risk AI can run an impact analysis to identify which tests need to be run to address these changes and quickly find any possible failure scenarios.
Lastly, when data input screens are split into two steps, requiring an additional navigational step to be automatically added, self-healing AI can step in and fix any test scripts that might fail and otherwise require human intervention.
Humans and AI-Assisted Automation
Additionally, leveraging AI-assisted automation relieves the full team – from non-technical users to seasoned test engineers – from manual efforts, allowing teams to focus on more complex and business-critical tasks and play a larger role in quality assurance. While combining AI and automation may seem like it will cut humans out of the chain completely, it actually makes them more critical than ever.
With AI-assisted automation taking over tedious tasks, like updating scripts, quality engineers now have ample time to focus on more strategic and critical tasks like product experience, test coverage, and more. Think of it like having a thousand free interns – you no longer have to do the mechanical tasks, but there's still a deep need to architect well and focus on processes, implications, and scale.
Ultimately, bringing together AI and automation elevates the type of work humans do and allows them to spend more time thinking critically while moving quicker and more efficiently.
Ensuring a Quality Future With AI-Assisted Automation
With the pace of innovation and digital transformation unlikely to slow down anytime soon, organizations need tools that will allow them to keep up, operate efficiently, and continue to serve customers to the best of their abilities.
AI-assisted automation is a powerful resource that optimizes and enhances various processes, including the critical quality engineering process. By combining visual AI, risk AI, and self-healing AI with automation, quality engineers can better identify and remedy issues within the application that may not have been caught otherwise.
Especially as quality engineering is such an integral and critical part of the software development lifecycle, utilizing AI-assisted automation can help elevate the most fundamental aspects of software engineering and create more powerful and expedited solutions.
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