Mav Turner, CTO of DevOps at Tricentis, takes a closer look at the use of AI in quality assurance to drive software development.
In today’s world, to be a successful ‘quality champion’ in software development, you don’t need to just be a well-skilled developer. It is also required to have the right mindset and some helpful quality engineering tools to differentiate your business.
Low-code/no-code test automation
Low-code/no-code technology allows organisations of any size to automate routine workflows using resources at hand. The rise of this type of automation has enabled individuals outside of the development team – including testers, business analysts and executives – to drastically improve quality when it comes to the app engineering and delivery process.
By implementing modular low-code/no-code frameworks, organisations can enable their teams to create workflows that fit their specific business needs, without compromising quality. Indeed, the global marker for low-code development technologies is forecast to grow by nearly 20% by the end of this year, representing an almost $27bn market opportunity, according to US technological research and consulting firm Gartner.
In empowering individuals to take responsibility for tasks that traditionally required professional developers, low-code/no-code software is beginning to transform the way many businesses operate. The knock-on effect is that DevOps teams can reduce their IT backlog and keep overall operating costs low while, at the same time, streamlining the development process.
Furthermore, this efficient approach can be further applied with low-code/no-code automated testing solutions, significantly reducing the time spent verifying platform updates. As a result, DevOps teams can maintain ever-changing workflows with the same levels of high-quality software, and drive more value from their technology investment.
Overall, testing solutions of this type provide the benefit of enabling increasingly stretched workforces to do and achieve more with less, without negatively having an impact on overall quality.
"A recent World Quality Report highlighted that 86% of CIOs and other senior tech leaders view artificial intelligence (AI) as one of the main considerations when selecting quality assurance (QA) solutions."
A recent World Quality Report highlighted that 86% of CIOs and other senior tech leaders view artificial intelligence (AI) as one of the main considerations when selecting quality assurance (QA) solutions. However, the ever-increasing role of AI across enterprises looking to increase efficiencies and more broadly in society as a whole is, of course, not without debate.
Of late, we have seen some of the most high-profile individuals across the technology industry argue the merits of AI and its future, with calls for better regulation at a government level among the main discussions points.
However, while questions will inevitably continue to be asked – and will remain – about AI adoption, it is also clear that the more successful enterprises are likely be those that are best at harnessing the array of tools available.
Research and advisory company Forrester highlights several different ways in which AI-powered automation can be applied to software testing and support digital acceleration towards better-quality software development, including:
Test design and optimisation – as organisations face pressure to innovate and deliver new apps, products, and solutions at an increasingly faster rate, many are still presented with the challenge of legacy tools and software. AI can assist with the review, assess, and increase coverage, increasing productivity and allowing teams to move faster.
Visual testing – most automated tools struggle to identify certain visual elements. Visual testing essentially looks at an application in the way a human would see it, better identifying issues when an application renders differently and in unexpected ways.
Intelligent bug hunting – AI can help organisations identify and analyse common failure patterns and highlight which tests should be prioritised, based on having the most bugs. Human intervention is still required however, because, in this scenario, AI is restricted to reviewing data around specific test case failure and not the entire application. At this point, AI is more of a helper application than something to which a review can be fully delegated.
Quality engineering communities
While quality engineering tools, such as those discussed above, are absolutely critical accessories in a quality champion’s toolbelt, another that cannot be overlooked is access to knowledge and people.
Thankfully, there are now a number of online communities dedicated to discussing quality management and quality engineering. All are accessible and open to every industry professional interested in applying quality best practices.
These resources play an important role in fostering an engaging learning space for quality professionals, developers, testers, executives, and other industry leaders, helping to encourage collaboration and the sharing of advice, knowledge and best practices.
They also play an important role in helping to drive best practices and move the industry forward. All quality professionals should tap into one if they are looking to foster a culture of continuous learning and improvement in a truly collaborative environment.
Want to learn more about AI in quality?
Read Robert Kurek's article: Artificial intelligence: A new revolution in quality manufacturing?
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