Are you taking full advantage of your test data?

Features matter a lot with any product or service, 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. But 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 SDLC are 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 like with product performance, the ability to analyze SDLC performance is predicated on the amount of data you have to for analytics.  The more data that you have, the better—and the best way to gather that data is by automating activities due to 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. However, teams often feel there is a lack of skills in-house to ensure all the working parts operate and interact seamlessly. While these challenges in regards to automating test cases is valid, test data should not be seen as a huge obstacle – a mountain to be scaled – because, 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, adding to these challenges is 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 AI and 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 results. Infostretch’s own Predictive QA solutions are part of ASTUTE, our new AI-powered software testing services suite for faster digital transformation.

Here are just some of the ways Predictive QA will impact your testing efforts:

Faster time-to-market

The first thing Predictive QA will help with is reducing test cycles so that organizations can get beautifully working 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 prioritize under-performing test use cases for greatest impact. And, it will enable them to better select the right types of testing and resources required for optimization.

To find out if Predictive QA is right for your organization, why not try our quick, no-obligation QE assessment. Alternatively, to talk directly to Infostretch about how predictive analytics and other AI-driven techniques can help advance your digital transformation goals, contact us today.

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