In today’s software delivery-focused enterprise, it is now critical for engineering leaders to master quality assurance to deliver impeccable software faster than ever before. Why? Because millions of customers around the world are using different devices, from different vendors, with different operating systems, and it is essential that businesses deliver flawless user experiences. After all, it only takes a split second, 50 milliseconds or less, for a visitor to develop an opinion of a website and decide whether to continue browsing or leave.
Business stakes are high and leaders must find ways to not only delight customers but also entice them to use their applications over their competitors. In order to deliver and build the perfect app, organizations have had to champion innovative practices that align business, research and development (R&D) and user experience. As companies continue to innovate and transition to digital, there is an evolution beyond agile software development and DevOps practices and shortening software delivery cycles from months or weeks to hours or less.
The Hurdles For Automated Software Testing
The problem is that automated testing hasn’t fully delivered on its promise. Automating the mundane, error-prone and repetitive manual tasks of software testing was supposed to be the holy grail of modern software delivery but it has since created more bottlenecks. Even the most rigorous testing cycles still don’t catch all the bugs, and even worse, the delivery of new features to customers is being delayed or not delivered at all.
Artificial intelligence (AI), however, is emerging to help take what is instrumental for the software delivery life cycle and finally make it work. The shift from heuristic algorithms to AI algorithms has replaced the logic developed by software engineers with algorithms that are self-evolving and self-improving.
Software delivery has evolved from slow, discrete and sequential to fast, continuous and parallel. A full delivery cycle now only takes hours, sometimes even minutes. Teams of developers are now coding, building tests, running tests, deploying to production, fixing small bugs and repeating the process faster than ever before. These new, innovative approaches have paved the way for collaborative software, full test automation and comprehensive test coverage. But even with all this innovation, testing is still a bottleneck to software delivery.
Looking back on the improvements of automated testing that have unfolded over the past few years, businesses must recognize that there is still a need for drastic improvement. In fact, in a 2018 survey, only 55% of organizations believed their automation program had so far been successful. More than that, over half of respondents found their automation program to be harder to implement than they originally anticipated.
AI And Machine Learning (ML) Can Open Software Testing Bottlenecks
For many software development teams and organizations, it’s difficult to find the correct test automation approach and, furthermore, it is often cumbersome to implement a test automation infrastructure. It often takes time to find the right tools and identify which tests are optimal for automation within the infrastructure. The real weakness of automated testing is that, for the most part, the test authoring, maintenance and results analysis are still manual. This is where AI and ML come in.
When AI and ML started to weave themselves into test automation practices, IT leaders could see the potential of autonomy in testing infrastructures. ML, a pattern-recognition technology, uses machine learning algorithms to predict trends. While testing creates an excess of information and logs, AI and ML tools cut through the noise and spot irregularities within apps.
AI has continued to unlock even more potential. Technology like visual AI, which we use in our automated testing solution at Applitools, can mimic the human eye and brain as it analyzes differences in what is displayed to consumers on computer screens, web and mobile applications and more. With millions of customers around the world using a variety and combination of browsers and device types, it’s easy to imagine how important visual perfection is to each and every company with a digital footprint.
AI and ML technologies are unlocking an understanding of the future and the potential for autonomy within software testing. AI has the ability to detect the root cause analysis for a specific problem, pointing developers to the exact piece of code that needs to be fixed. With these evolving technologies and methodologies, AI algorithms will think for themselves and create and run tests without any manual user intervention.
Autonomous Testing And Why It’s Important
Autonomous testing is the automated creation of tests, the maintenance of tests and analysis of the results with the computer acting and thinking like a human brain. This kind of autonomy equals testing without coding. Instead of writing hundreds and thousands of lines of code, autonomous testing allows tests to be created autonomously without much human intervention and can reduce the workload for engineering teams.
Futuristically speaking, software has the potential to inject its own required data, simulating human testing behavior, before running the necessary tests for the application to be completed. The efficiency gains through autonomous testing can shrink the time to market for new digital products and apps considerably.
The way I see it, autonomous testing is critical for the ubiquity of digital transformation. As engineering teams continue to innovate around their testing and quality assurance processes, a new era of expanded testing coverage, increased software release velocity and superior visual quality will take hold. IT leaders may look back on the current automated testing reality and lean into the autonomous future.
Leading brands are already implementing AI as part of their testing processes and are making progress toward autonomous testing. Those that aren’t on a path to autonomous testing are likely to be unable to keep up with the pace of change to remain competitive in a business landscape that requires digital everything.