What is the future of QA and how we can optimize the QA process to make it easier for ourselves to guarantee software that behaves in ways we want. Here are the five steps from only testing the system to understanding the usage of the software from human written tests towards measuring business KPIs and using these to evaluate the quality of the system.
Qentinel has received a 420.000 euro grant from Business Finland as part of an larger international ITEA3 consortium, a transnational and industry-driven programme in the domain of software innovation. Goal is to develop DevOps quality analytics and visualization with help of AI.
1. AI-augmented test analysis and optimization
Testing by itself is done to validate the software solution being developed and guarantee a certain agreed upon level of quality for the product and to make sure no major regression happens in the software.
Machine Learning methods will allow us to bring a larger amount of statistical analysis tools to bear on all the test and telemetry our software pipeline is producing. We will see a surge of different software vendors bringing solutions for different kinds of dashboards and visualizations to provide a way to analyze the validity of our software and provide metrics for just about anything.
Computer vision is running “under the hood” in many consumer applications, but it’s also being used to help tech professionals address tricky software problems and open up new avenues for business efficiency.
So you just completed the great Elements of AI course by the University of Helsinki and Reaktor and got really interested in AI and yearn to learn more.
If you’ve ever had anything to do with banking, financial services, insurance or even health care, chances are that you’ve unwittingly come within range of what’s known as RPA, or Robotic Process Automation.
