Data Collection

Why Millions of Data Points?

True Automation is impossible without building a test definition model with deep learning and massive data sets.

Automated UI test scripts are notoriously expensive to maintain. They constantly break because each step is made up of brittle parts: brittle elements that are rigid to change and brittle actions that neglect how the application actually behaves.

Automation is supposed to save time. Yet these broken tests require manual fixes and slow down the speed of releases.

We fix exactly that.

We don't use rigid selectors

Instead, we capture multi-dimensional state of the entire page on every test run.

We do not use a single selector like the ID or X path which would break.
In fact, we don't use backup selectors which would slow down the execution and we certainly don't ask you to weight the likelihood of different selectors, which would be an annoyingly manual effort.
On test creation: Architect captures the entire state of the web page at every single step in the test. We collect hundreds of attributes per element, not just for the target element in the test step.

What is being collected?

We collect this data for every element on the page of every step of the test. An average web application has nearly 2500 elements on each page. So for this ten step test, we've collected an average of over 10 million attributes and that's only the data from the initial test creation.

Locations on the page
Structural position
Scrolling data
Timing data
Pre, post states
Relationship to code
CSS properties
Visual styles
Context & relation to other elements
Network metrics
Screenshots

Our Smart Element recognition uses this enormous amount of data to accurately identify objects with over 99.9% accuracy.

Test Automation Benefits

Deep Learning is absolutely essential for test automation

When you first see the test, Architect applies our generic machine learning models which have matured over the years, using terabytes of data from millions of tests and thousands of different applications.

But it's not only that. Functionize learns from your data and trains these machine learning models over time. So as more data is captured, Functionize continuously learns to reduce the number of errors.

And not only is machine learning responsible for correctly identifying elements. We also use machine learning for each action when running your test.

Different ML models are used for different purpoces in order for test automation to work

You also need ML while running your tests

Traditional test actions are evaluated using a static and narrow definition. Too many tests fail for unnecessary or unknown reasons. If a human would manually run through the test, they would pass. But with automation, they fail. This is because test scripts are dumb and miss so many different dimensions that would be obvious to a human tester.

If a human would manually run through the test, they would pass. But with automation, they fail. This is because test scripts are dumb and miss so many different dimensions that would be obvious to a human tester.

Functionize uses machine learning to take automation to the next level and mimic human like behavior. This creates significantly more robust tests that are less likely to break.

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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

Data Collection

Functionize future-proofs your tests using machine learning.

Millions of datapoints

99.9% accuracy

Dynamic learning

Why Millions of Data Points?

True Automation is impossible without building a test definition model with deep learning and massive data sets.

We don't use rigid selectors

Instead, we capture multi-dimensional state of the entire page on every test run.

Deep Learning is absolutely essential for test automation

You also need ML while running your tests

Download Datasheet

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