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Description

Examine the causes of failures right after the testing phase concludes. Create straightforward and easy-to-read reports for your teams. Utilize machine learning-driven auto-analyzers to delve into the reasons behind the failures. Consolidate test outcomes from different platforms, frameworks, and programming languages while delivering actionable insights. Employing machine learning algorithms helps to uncover patterns in the test data, identify the underlying causes of failures, and forecast future testing outcomes. Support the manual examination of test logs and emerging failure patterns from the latest test runs. Enable automated decision-making processes for release pipelines by adhering to defined testing criteria and outcomes. Present test results in a clear format that facilitates monitoring of trends, recognition of patterns, generation of insights, and informed business choices. Regularly assess your product's health and automate release decisions with Quality Gates to enhance efficiency and reliability. This approach not only streamlines the testing process but also significantly contributes to improving overall product quality.

Description

Reduce costs. Reduce costs by making use of your infrastructures to their full potential. Branding helps you to anticipate stability and performance issues and improve customer satisfaction. Anticipate your growth. You can plan better by knowing the limits of your applications before you invest. We have more than 500 campaigns of tests on multiple technologies and environments, and load tests that simulate millions of visitors per day, so we can handle even the most complicated campaigns. It is difficult to perform performance testing and requires experience in order to obtain relevant and useful results. There are many obstacles to overcome, including poorly designed scenarios, misinterpreted results, and improperly configured test servers. Our experts can assist you in all areas to ensure that you get the most out of your campaign and investments. Analyzing the results and making recommendations after or during the tests to improve your application or infrastructure.