As organizations across all industries face opportunities and threats in the new digital era, they increasingly look to leverage their full power of the data they possess. Data is a treasure trove for enterprises, and big data mining has grown into much more than a sales tool. Advanced, predictive analytics are now used to hone processes, improve product quality and deliver those products more rapidly.
Companies that are serious about digital transformation know the importance of big data. And, increasingly they are turning their attention to predictive analytics techniques as a means of assessing the whole product life cycle. In test automation terms, there are several benefits from a predictive QA approach: Getting the most out of the defect management and test automation data enables you to create better models, optimize testing processes and predict defects like never before.
Features matter a lot with any product or service, of course, but what keeps users truly engaged is the overall experience. More than ever, that experience is dictated by performance—and why product performance matters so much.
In today’s market, a company that can deliver quality products to market faster than its competitors gains a significant advantage. So, now the performance of teams in the software development life cycle (SDLC) is being closely analyzed to deliver quality at speed. However, enterprises that prioritize product performance over SDLC performance are not taking full advantage of the possibilities digital has to offer.
Just as with product performance, the ability to analyze SDLC performance is predicated on the amount of data you have to for analytics. The more data you have, the better—and the best way to gather that data is by automating activities, mainly because of the vast traceability and identification capabilities that technology provides. Because of this, automating testing activities is one of the biggest activities that organizations are implementing to enhance their SDLC performance. This is, in fact, a key driver in enabling digital transformation within any organization.
However, the issue we see over and over again is this: The sheer quantities of testing activities that need to be automated are actually slowing organizations down. In part, testing complexity is to blame. Teams often feel there is a lack of skills in-house to ensure all the working parts operate and interact seamlessly. While these challenges with regards to automating test cases is valid, test data should not be seen as a huge obstacle, a mountain to be scaled. With the right tools, that test data is an enabler to optimizing performance and holds the keys to the future success of your product through the analytics it can provide.
Yet, compounding these challenges is the fact that toolsets have a habit of changing, so the data isn’t always structured uniformly. Now, organizations are going a step further with predictive analytics, powered by artificial intelligence (AI) and machine learning (ML) techniques, by leveraging their large volumes of structured and unstructured data available from defect management tools and test automation results.
This means organizations are now able to successfully analyze SDLC performance by being able to incorporate data from all their testing activities to give them predictive QA capabilities.
How predictive QA gives organizations an advantage over traditional QA comes in the form of intelligence. Predictive QA converts test data into actionable insights.
Here are just some of the ways predictive QA will impact testing efforts:
- Faster time-to-market
- The first thing predictive QA will help with is reducing test cycles so that organizations can get top-tier software into customers’ hands faster. Predictive QA calls out inefficiencies, optimizes testing efforts and helps cut down on test cycles. We recently worked with a leading hotel chain that was able to boost test automation efficiency by 30 percent using AI and ML-driven predictive analytics.
- Better defect detection
- Defect detection is one of the first things that comes to mind when we talk about improving quality, and it is certainly true that predictive QA can do that with the available data. Using predictive techniques, software teams can drill down to understand root causes and failures, predict defect ranges and the risk of modules for future versions.
- Understand what’s working (and what’s not)
- With predictive QA, teams get unprecedented insight into what’s going on in their testing efforts. Using these techniques, teams can assess what is driving greater application lifecycle efficiencies—and they will plainly be able to see what is not. They will be able to address under-performing test cases and prioritize those with the greatest impact. And, it will enable them to better select the right types of testing and resources required for optimization.
The growing demands of digital are pushing enterprises to the limit, requiring faster release cycles that consume a significant amount of resources. According to the latest World Quality Report, 99 percent of organizations face challenges testing in their agile environments while the average level of automation is only around 16 percent.
Predictive analytics is beginning to address this in many ways. Invisible to the consumer eye, our increasingly connected world is reliant on accurate, actionable, automated analytics to power it. Predictive QA has proven essential in helping leverage machine learning and advanced analytics techniques, targeting both end-product and the design life cycle as a whole.
Enterprises are looking to AI to exploit the wealth of data found in various levels of their IT organizations, whether at the software development level or on the production side, said Dominique Raviart, IT Services practice director for analyst firm NelsonHall. “With AI, they have an untapped opportunity to better identify where defects lie, increase the quality of applications, and ultimately improve the user experience of end-users, customers, and other stakeholders.”
