Artificial Intelligence and the latest testing technologies
By Rafik Baghdasaryan
The 2018–19 report states that the growth of artificial intelligence has a big impact on QA activities and will continue to grow in the upcoming years. According to the World Quality Report, AI will “enable companies to transform testing to a fully self-generating, self-running and self-adapting activity.” In order to achieve that, you need to reach proficiency in automation.
“The main purpose of applying AI in QA is to create a smarter and faster testing architecture that runs and adapts itself automatically to application changes.”
What does this mean when it comes to practice? To put it simply — you can use AI to apply smart analytics to the most important decisions in the testing process, for example, task assignment optimization, test data generation, test case design approach, test execution priorities or to generate optimal test reports. Beyond that, AI will be involved more and more in the execution phase, which is the most critical path of product validation.
This is the natural way of transformation for QA and testing activities. Business demands fast delivery, a quick reaction to changes, high-quality products with optimized costs — therefore, forces us to introduce these smart solutions. Some are available as ready-to-use, off-the-shelf products (basically all Application Lifecycle Management tools) and others might be developed/enhanced in-house.
What are the predictions for the future?
“The latest software testing trends have seen significant growth in the use of open-source automation tools, at the expense of some commercial products. It’s partly about cost, of course, but also about flexibility, and about solving problems collegiately within the Open Source community.”
The future of software testing is faster tests, faster results, and most importantly, tests that learn what really matters to users. Ultimately, all testing is designed to make sure the user experience is wonderful. If we can teach a machine what users care about, we can test better than ever before.
Conventionally, testing lags development, both in speed and utility. Test automation is often a weak spot for engineering teams. ML can help to make it a strength.
What ML means for the future of software testing is autonomy. Smart machines will be able to, using data from current application usage and past testing experience, build, maintain, execute, and interpret tests without human input.
It’s likely that not all aspects of software development should be automated. Given a long tradition of E2E testing being driven primarily by human intuition and manpower, the industry as a whole may initially resist handing the process over to machines. Across practically every industry, insiders contend that machines could never do a human’s job. Those who have resisted the rise of ML and doubled down on human labor often find themselves left behind.
A familiar story is unfolding in the world of testing: ML-driven test automation is in its infancy today, but it is likely only a few years away from taking over the industry.
Autonomous End-to-End Tests
Machine Learning’s core advantage in E2E testing is being able to leverage highly complex product analytics data to identify and anticipate user needs. ML-driven testing is able to watch every single user interaction on a Web application, understand the common (and edge) journeys that users walkthrough, and make sure these use cases always work as expected.
If that machine is testing many applications, then it can learn from all of those applications to anticipate how new changes to an application will impact the user experience. ML-driven testing can already build better and more meaningful tests than humans thanks to this data.
The tests developed by ML-driven automation are built and maintained faster and far less-expensively than test automation built by humans. Such testing leads to much faster (and higher quality) deployments and is a boon for any VP Engineering’s budget.
What About Testers?
What about the people currently doing these jobs?
Quality engineers still have a major role to play in software development. The most efficient way to assure quality in software is to embed quality control into the design and development of the code itself. Testing only exists because that process is imperfect.
As ML takes over the burden of E2E testing from test engineers, those engineers can use their expertise in concert with software engineers to build high-quality code from the ground up. From our own interviews on the matter, it seems most quality engineers would far prefer this to grind away at test maintenance all day.
The Future Looks Bright
ML offers a more streamlined and effective software testing process. It establishes a process that’s better equipped to handle the volume of developments and create the needed specialized tests. Smart software testing means data-based tests, accurate results, and innovative industry development.
Today’s fast-paced evolution of enterprise application testing environments shows no sign of abating. As it continues to evolve, Quality Engineering will be defined less by its focus on the applications themselves, and even more by its alignment with business purposes. That means using real-time testing, monitoring, and fixes to ensure the business “works” as well as the code. And it means developing self-learning, self-adapting systems supported by machine learning and advanced analytics.
