Quality Assurance Evolution and Analysis with AI / ML
In this age of explosive technological acceleration, cognitive technologies are converging to redefine how knowledge is synthesized, disseminated, and analyzed.New Technology platforms and tools, new methodologies with shorter development time and a constant demand to remain competitive and innovative has put the focus back on how testing is being carried out. We have seen usage of cognitive technologies in automation and performance test which helps achieve a level of auto-healing capability. It has been observed that testing teams have evolved to cope with demand and are using AI based testing approach to reduce the overall maintenance and attain optimum efficiency. However, test implementations are majorly executed in-silo (per engagement), or they are reactive rather than proactive.
So, what can an organization or a test team do to make testing and overall test implementation more effective and how can we use the data from multiple testing engagements and make testing predictive and proactive?
In this point of view, we will discuss overview of AI, evolution of quality assurance and discuss few of the scenarios where we can use cognitive analysis to optimize overall testing.