Testinium AI (Mobile)

Testinium AI, is a product that produces results by analyzing user motion logs generated by the end user and making inferences with machine learning. After the frequently repeating popular patterns are detected by data mining techniques, the detected flows are transformed into test automation scripts.

So What Do We Aim With This Application

  • To reduce human errors and software costs to acceptable levels by minimizing the human factor in software testing processes.
  • To reduce exponentially increasing project costs by detecting errors as early as possible.
  • Instead of producing human-factor manual test flow scenarios, it aims to produce data-oriented possible test flow scenarios with machine learning algorithms.

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    We have realized the risks after the structural test transformation and started managing effectively. We ensured that our product is being developed via a test-driven process
    Intertech
    We are really impressed by the quality of the product. The wonderful team effort of Testinium helped us to improve our project. With their professionalism, prompt response, courteous service and excellent automation solutions, we are able to test the product fully and improve our customer satisfaction. We look forward working with them on future projects.
    Allianz
    Our aim was to increase our test coverage without increasing our software testing cost. Testinium helped us to make real.
    Turkcell
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    Expectations from Testinium AI

    • Increasing tests to high capacities with minimum manpower and significant optimizations.
    • Creating a separate set of frequently used tests within the scenarios provided by the users, thus ensuring that the test data that needs to be managed is kept at an optimum level.

    A Faster, More Scalable Test Strategy with Testinium​

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    Innovative Aspects of the Testinium AI

    Thanks to Testinium AI, human workload in traditionally created manual and automation tests will be minimized and comprehensive and important test set scenarios will be automatically created. Thus, by analyzing user behavior data, repeating popular and hidden patterns will be found and test scenarios will be automatically created and classified.

    It is ensured that optimized test scenarios are obtained based on the determined patterns and the automatic generation of test scenario codes with this. The results of the test scenarios converted into codes are analyzed and various information is provided to the users. In this way, users achieve healthier results.

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