Data Science

Our platform leverages many forms of data science as well as artificial intelligence.

Template recognition in Testing Automation

Template recognition

Our Modeler records every interaction taken by every user in your system

Boosting ML

Boosting ML

By combining different models we achieve higher testing accuracy

Predictive models

Predictive models

We use long short-term memory models to predict next steps with 85% accuracy

Functionize Platform

No machine innately knows your application as well as your team and your users. Functionize boosts the power of that insight with AI allowing you to get the test coverage and release confidence you need without an army.
We use data science - multiple ML models - on test creation, test maintenance and analytics

ML Modeling Benefits

Functionize Platform

No machine innately knows your application as well as your team and your users. Functionize boosts the power of that insight with AI allowing you to get the test coverage and release confidence you need without an army.
01
Understand real users

Understand real users

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.

02
Identifying unique user journeys

Identifying unique user journeys

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.

03
 Enhancing AI

Enhancing AI

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.

04
Coping with sparse data

Coping with sparse data

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.

05
Enabling template recognition

Enabling template recognition

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.

Boosting

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.

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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.

Learn how Functionize intelligent testing platform can help you:

  • Create AI-powered tests using Architect or plain English via natural language processing
  • Reduce test maintenance time by 85% with self-healing tests
  • Scale test execution and run cross-browser tests in parallel
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