One big topic at least in our company has been the discussion on how cloud based test automation affects your business and how it can drive transformation, and performance in your company.
Is your current deployment frequency choked by your testing speed? Is regression testing driving you to boreout? Do you think test automation is a tough cookie to crack? Have you wondered on how to best leverage robots and humans in software testing? Are you benefitting from the speed of robots and ingenuity of humans? Are you utilizing your manual testers’ capabilities well? If you are a tester, a developer, a test manager or a product manager you probably deal with these questions in your everyday life.
Software is growing more and more ubiquitous with our daily living with each passing year. We grow more dependent on software for our daily interactions, from connecting with friends to handling banking.
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.
A reasonable approach to test company’s business critical information systems such as ERP depends on the visibility to product development. If the system is developed in-house there's likely a good visibility to actual development teams’ work and quality assurance can be involved in the very early phases of product development. Involving QA early can be referred to as shift-left. On the other hand, a system used as a cloud-based Software-as-a-Service (SaaS) without any visibility to development is restricted also from the testing point of view. Only production version testing might be possible, often referred to as shift-right.
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.