Predictive Analytics & Intelligent Automation in QA | BlueSwan

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Predictive Analytics Intelligent Automation in
QA with BlueSwan
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Predictive Analytics Intelligent Automation in
QA with BlueSwan
How Predictive Analytics can move your QA
organisation one step closer to Continuous Testing
 In our world, organizations developing and
deploying applications face mounting pressure to
deliver better solutions faster while incessantly
churning business requirements.  Agile and
DevOps application delivery methodologies have
had an enormous impact on market responsiveness
and Continuous Delivery has augmented ITs deliver
y arsenal. Despite these improvements, many
companies still face the headwinds of aligning
and optimizing their testing resources and
struggle with ever-compressing test cycle times.
Software delivery teams are not only tasked with
delivering assurance faster but also need to be
more intelligent about what they test and develop
cognitive capabilities that predict and prevent
application failures from surfacing before
applications reach testing. This article examines
the use of analytics in the QA function and
explores how it must become a critical component
of continuous testing.  Progressive application
delivery is increasingly gaining adoption
across enterprises  In the past, many
organisations relied on QA personnel and tools to
help ensure application quality and drive
business value. Such traditional testing
approaches leveraged human resources to execute
massive amounts of application testing tasks
manually with the assistance of software
technologies. However, while such conventional
approaches for application testing raised quality
levels and reduced the probability of defect
leakage into production, they were inherently
inefficient, had difficulty proving value, and
were fragmented in their ability to predict
application testing issues before they became
embedded defects.
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Predictive Analytics Intelligent Automation in
QA with BlueSwan
Progressive application delivery is increasingly
gaining adoption across enterprises  In the past,
many organisations relied on QA personnel and
tools to help ensure application quality and
drive business value. Such traditional testing
approaches leveraged human resources to execute
massive amounts of application testing tasks
manually with the assistance of software
technologies. However, while such conventional
approaches for application testing raised quality
levels and reduced the probability of defect
leakage into production, they were inherently
inefficient, had difficulty proving value, and
were fragmented in their ability to predict
application testing issues before they became
embedded defects. To address this problem,
organisations needed not only the means to enable
rapid remediation but also the capability to
automatically detect software incidents. These
ensure that customer-facing or internal business
applications are always available and running to
support users when they are needed.  Read Full
White Paper at https//www.cigniti.com/resource
/white-papers/predictive-analytics-for-qe-with-blu
eswan