In Back to the Future, Marty McFly realizes how small changes in the past can have a big impact on his own future. The movie plays with the notion that defining your future depends on correcting whatever has gone ‘wrong’ in the past. To the best of my knowledge, humans haven’t yet managed to create anything like Doc Brown’s DeLorean time machine. But right now, QA teams are experiencing their own slice of Back to the Future as they increasingly engage the power of data analytics combined with machine learning to not only describe what’s happened, but to predict and prevent future defects.
Today’s QA teams are faced with huge amounts of data, both structured and unstructured, coming from defect management tools and test automation results. It’s a lot to deal with and a lot of it goes unused. Thanks to advances in data analytics, AI and machine learning techniques, enterprises can now ensure they glean far more intelligence from their test data.
Roughly speaking, test data falls into three categories: data relating to the user experience, data relating to lifecycle efficiency, and optimization data. QA teams need to assimilate data that is quantitative (fix SLAs, blocker bug rates or production bug rates) at the same time as data that’s much more subjective (relating to the user experience). As software teams are only too aware, quality at speed is what counts in today’s digital experience, which is why efficiency and optimization data also play such an important role in determining overall quality.
In Back to the Future, it was clear to Marty that he had to ensure his parents fell in love at the school dance or his own future was in jeopardy. Fast forward to test data in the 21st century, and the correlations are not that obvious. That’s where predictive analytics in QA (or Predictive QA) comes into its own. Using AI, machine learning and algorithms for predictive analytics, Predictive QA helps software teams accelerate and improve their offering in several important ways.
First off, Predictive QA tools enhance an organization’s current defect management data. However, it doesn’t stop there. As the name suggests, a huge part of predictive analytics is the ability to use the current data to “see” the correlations and predict future defects. Anticipating defects early in this way speeds cycle times. Predictive analytics techniques can also use natural language programming to interpret application feedback, predict and fix defects, and improve the user experience.
With Predictive QA, these are some of the impacts organizations can expect:
Infostretch recently worked with a leading hotel chain offering omni-channel digital experience to customers. While the advantages and capabilities of such an offering were huge, one challenge that resulted from the omni-channel model was increased complexity, resulting in longer regression times. By harnessing the power of AI and machine learning, the company was able to increase their test automation efficiency by 30 percent, along with improving test coverage and optimizing resource allocation. Thanks to Predictive QA, the company was able to identify slack in their SDLC that could reduce their QA headcount by 20 percent based on module risk assessment.
If your organization’s approach to defect detection feels more like Groundhog Day than Back to the Future, Predictive QA might just be right for you. To find out if Predictive QA will help you get the most out of your test data, take our quick, no-obligation QE assessment. Infostretch’s Predictive QA tool is part of ASTUTE, the new AI-powered software testing services suite for faster digital transformation. To learn more about how Predictive QA and ASTUTE can help you reach your digital transformation goals, contact us today.
Image source: https://commons.wikimedia.org/wiki/File:Back-to-the-future-logo.svg
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