Roadmap for Test Studio

What's Coming in 2019?

Take a sneak peek of the high-level Roadmap containing the major features for the year. Along with them, there will be smaller user-requested features and improvements.

R2 2019 (Released)

Image-Based Element Detection

Elements with dynamic IDs are a challenge for any automation framework or tool.
Image-based element detection can overcome this problem and in combination with our existing element detection logic offers full support for even the most challenging to automate applications.

Test Studio uses a combination of attribute and image-based identification logic which makes tests even more stable. In the case when one of the identifications fails, the Runner will find the element by its other identification thus saving the test from failure.

See more...

R3 2019 (Expected in October)

Web-Based Test Results and Reports Dashboard

Usually the Manager/Release Decision Maker/ Product Owner persona would like to have constant access to test automation results to determine the product health.

Test Studio's web results and reports will unlock this with a beautiful dashboard showing test list results historically and a custom reports section. Anyone with access to the web address will be able to monitor the results.

Image-Based Elements Improvements

-Fix wrong image capturing of certain elements

-Browser scaling support

-Add ability to switch the primary element identification to image

Support for Microsoft’s Chromium Edge Browser

Test Studio's team aims day-one support for the new Microsoft browser on the way.

R1 2020 (Expected in February)

Self-Healing Hybrid Elements

Decreased test maintenance time by attempting to automatically fix failed identification by engaging the user in a conversation with multiple choices and building ML model based on the gathered data. If the primary Element identifier fails, but Test Studio finds the element by one of the other identifiers, it knows that the element is still there and may attempt to fix the primary identifier so that it works on the next run.

This results in stable tests with several healthy identifiers and will minimize the time spent in debugging the failing element.

Smart Suggestions Based on Cognitive Modeling

Using machine learning algorithms Test Studio will detect weaknesses in the identification logic even when it doesn’t fail and will improve it automatically or if user input is required, will report them back to the user providing suggestions for improving the stability of the automated tests.

DISCLAIMER: We operate in a dynamic environment, and things are subject to change. The information provided on these and related pages is intended to outline the general Telerik product direction. It is intended for informational purposes only and should not be relied upon when making any purchasing decision. We may decide to add new controls at any time depending on our capability to deliver products meeting Telerik quality standards. The development, releases and timing of any features or functionality described for Telerik products remains at the sole discretion of Telerik. These roadmaps do not represent a commitment, obligation or promise to deliver any products to the customer at any time.

Roadmap