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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

TestBooster.ai is an innovative test automation platform that harnesses the power of AI to facilitate the creation of automated tests for both web and mobile applications, enabling teams to write tests using natural language without the need for coding or selector manipulation. Users can articulate their testing requirements in straightforward English or Portuguese, while the intent-driven AI understands these directives and performs comprehensive tests across various environments. The platform intelligently adjusts to changes in layouts or UI components, which minimizes instances of broken tests and alleviates the upkeep typically associated with selector-driven tools. TestBooster accommodates a wide array of testing types, including regression, functional, usability, response time, API, code-based, web, and mobile testing, all within the context of continuous testing practices. Teams have the ability to monitor test executions live and gain access to extensive documentation that features screenshots, logs, results, and practical recommendations. In the event of a failure, it pinpoints the exact location of the issue and offers suggestions for improvement, empowering teams to analyze and refine their testing processes effectively. This comprehensive approach not only streamlines testing but also enhances overall quality assurance efforts across the board.