Our Modeler records every interaction taken by every user in your system
By combining different models we achieve higher testing accuracy
We use long short-term memory models to predict next steps with 85% accuracy
Real users never behave as you would predict. By tagging and recording every interaction on your UI we are able to give you deeper insights into how users actually interact with your application. This can help define new tests, but can also assist your product team to identify UX problems or unused flows.
Having identified user flows, we are then able to predict next steps for users with high accuracy. This is a powerful tool, as it also allows us to improve our understanding of how your application works.
By itself, AI is pretty dumb. To work properly, it needs as much data as possible. Using data science approaches helps us to extract additional data from your application and thus acts to further boost our AI models.
When you start creating tests for a new application, our system has no past history to go on. So, we have to use any techniques we can to enhance the information available. Often, this means we turn to classic data science for assistance.
Our template recognition approach relies on knowledge of how UI elements are typically grouped together. However, the link between elements isn’t rigid e.g. dates are sometimes MM/DD/YY, sometimes DD/MM/YYYY, etc. So, we identify these relationships by using data science techniques like Akaike Information Criteria.
For machine learning and AI to reach its full potential, other elements of supervised learning are often required to “boost” model performance and optimize results. Boosting is a term where weak models are made into strong models. Adaptive Boosting is a machine learning meta-algorithm that can be used in conjunction with many other types of learning algorithms to improve performance.Nowadays, boosting techniques are used to help solve a wide range of AI problems. For instance here at Functionize, we use an autonomous intelligent test agent to run all your automated tests. This test agent uses multiple forms of artificial intelligence. Many of these rely in turn on boosting. For instance, our ML engine uses computer vision as one way to identify and select elements on the screen.
AI-Powered Testing
Functionize is the industry’s most advanced enterprise AI-powered testing. We help teams break through testing barriers and enable organizations to release faster.
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