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What is Zebrunner?

Zebrunner is a cutting-edge platform that harnesses AI to merge manual and automated testing, fostering effective collaboration among QA teams. Its AI-driven features enhance testing processes by creating and completing test cases automatically, prioritizing issues, and providing immediate insights into testing activities. The Test Case Management aspect of Zebrunner presents a robust solution for structuring manual test cases, boasting user-friendly split-screen navigation, drag-and-drop editing, and customizable layouts that enhance management efficiency. With the ability to track progress in real-time, testers can swiftly pinpoint and address any obstacles or failures that arise. Additionally, the platform allows for the creation of custom fields and filters, ensuring that test management can be adapted to meet the specific needs of various projects. Furthermore, Zebrunner's Automation Reporting improves automated testing by offering sophisticated reporting and analytics capabilities. It seamlessly integrates with widely-used frameworks like TestNG, JUnit, and Cypress, allowing teams to gain real-time visibility into their test executions. This enables them to monitor progress effectively, scrutinize failures, and access detailed logs or video recordings. By providing thorough reporting functionalities, Zebrunner equips teams with data-driven insights that not only optimize their testing strategies but also empower them to make better-informed decisions moving forward. Overall, Zebrunner's comprehensive features contribute significantly to enhancing the overall quality assurance process.

What is ReportPortal?

Investigate the reasons for failures immediately following the completion of the testing phase. Generate concise and comprehensible reports for your team members. Leverage machine learning-powered auto-analyzers to explore the factors contributing to the failures. Aggregate test results from various platforms, frameworks, and programming languages, ensuring that actionable insights are provided. By utilizing machine learning algorithms, you can reveal trends within the test data, pinpoint the root causes of failures, and predict future testing results. Complement the automated analysis with a manual review of test logs and new failure trends observed in the most recent test executions. Facilitate automated decision-making in release pipelines by following established testing standards and outcomes. Display test results in a manner that enhances the tracking of trends, allows for the identification of patterns, generates insights, and supports well-informed business decisions. Conduct regular evaluations of your product's status and automate release choices using Quality Gates to improve both efficiency and reliability. This comprehensive strategy not only simplifies the testing process but also plays a vital role in elevating the overall quality of the product while ensuring teams remain informed and proactive.