3 Ways to Embrace Intelligent Testing - Qmetry

3 Ways to Embrace Intelligent Testing

Agile , Artificial Intelligence , Intelligent Test Automation , Software Quality , Test Automation May 24, 2018

We live in the era of exponential digital transformation. The blink-and-you-miss-it pace of technological changes has disrupted every aspect of the economy. The key enablers of this transformation are software development and quality assurance. And to adapt to these extreme shifts and survive the changes, both software development and testing are in a state of flux.

Intelligent testing and Intelligent Test Automation are two of the hottest trends that are helping businesses address the unpredictability and chaos. But what does it really entail?

Put simply, intelligent testing uses the combined power of AI, machine learning and analytics to remove uncertainty, eliminate redundancy, increase coverage and traceability.

How businesses can leverage Intelligent Testing

End-to-end coverage

Intelligent Testing relies on the use of Machine Learning and BOTS to simplify the necessary but complex and repetitive tasks. BOTs can simplify every stage of the testing lifecycle to remove redundancy, increase coverage and focus on the essential. This enables to clear backlogs faster, predict and prevent failures and improve the overall outcomes.

Use ML to improve customer experience

Machine learning provides instant and rapid data-led feedback/insights that result in continuous feedback and learning, as more data is added to the models. Customer experience is the core differentiator in this highly competitive landscape. Thus, cognitive computing and ML have the answers to some of the most pressing needs of modern enterprises. That is, faster response times, higher level of accuracy to wading through reams of test data collected from multiple sources.

Leverage cognitive defect prediction and prescriptive analytics to thwart risks An intelligent testing model is an adaptive one. It is continuously learning and uses the feedback loop to improve the overall software quality. AI and ML can identify common causes of failure and high-risk defects. These technologies can categorize errors more accurately providing heat map visualizations and high-risk areas. Intelligent QA tools thrive on information. They mine large volumes of structured and unstructured data from defect management tools and automation test results. The tools then use these to predict outcomes and suggest actionable insights. The job is no longer simply to validate, but to automatically detect and regressions and high-risk defects in apps.

It is with automation however that intelligent testing achieves its potential. Intelligent test automation uses model-based testing and TDD/BDD based automation site for optimized performance. It prioritizes execution based on changes to ensure coverage in a short time and allow in-sprint testing, It allows faster classification of results automatically with AI and ML to increase the response time.

Automation alone cannot thrive in the break-neck pace and increasing complexity of the digital transformation era. It is therefore imperative to support test automation initiatives with intelligence – data back insights, coverage analytics, reusability traceability and validation powered by AI and ML.

If you would like to know more about how you can use Intelligent Testing and Intelligent Test Automation for your business, our test automation experts can help.

Deepak Parmar
AUTHOR

Deepak Parmar

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