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.

Test Creation

Test Automation

Test Analysis

Test Creation


Define Test Objective

Reinforcement Learning Markov Decision Process Q Learning

Install Javascript Tag

Unsupervised Learning Expectation Maximization Local Sensitivity Hashing LSTM Model

English Test Workflow

Word Embeddings Element Embeddings Neural Networks

Talk Test Steps

Speech to Text Time Segmentation NLP Modeling

Test Automation

Markov Decision Process

Computer Vision Template Recognition

Probability Mass Functions

Unsupervised Clustering

Robust Test Model

Multinomial Logistic Regression

Convolutional Neural Networks

Supervised Learning

Recurrent Neural Networks

Runtime System in Cloud

Data Modeling
Data Cleaning

Test Analysis

Test Warnings
  • Visual Anomaly detection
  • Element Anomaly detection
  • Timing Anomaly detection
Root Cause Analysis
  • Decision Tree
  • Neural Networks

ML Modeling Benefits

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.

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.

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.

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.

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.


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 Adaptive Event Analysis™ engine uses computer vision as one way to identify and select elements on the screen.

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The Functionize platform is powered by our Adaptive Event Analysis™ technology which incorporates self-learning algorithms and machine learning in a cloud-based solution.

For more information, read our blog, follow us @functionize or email us to learn how you can get started today with Functionize Intelligent Testing.

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