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.

Download Datasheet

Download our datasheet to discover why Functionize is the #1 Enterprise AI-powered testing platform. In our datasheet, you’ll gain insights into:

Advanced Deep Learning: Learn about our industry-leading ML models that offer unmatched accuracy and adaptability in QA testing.
Comprehensive Testing Capabilities: Explore our all-in-one platform that supports a wide range of testing needs, from APIs to email, across multiple environments.
Autonomous Operations: Understand how our platform’s autonomous test creation and self-healing capabilities reduce manual effort and enhance test coverage.
Scalable Cloud Infrastructure: Learn how our cloud-based testing infrastructure allows you to perform tests at scale, efficiently, and cost-effectively.

Data Collection

Advance towards testing autonomy with Generative AI, blending efficiency with cutting-edge technology.

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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