Data-driven testing separates test logic from data, allowing multiple input variations to be tested efficiently. Meanwhile, keyword-driven testing simplifies automation by defining reusable actions, making it accessible to non-technical testers. In this blog post, we compare both methodologies and show you which one you should choose.
Test coverage is a key metric for assessing whether your test cases thoroughly validate an application’s functionality and requirements. Without a structured approach, teams risk missing critical scenarios and leaving defects undetected. Learn how Xray can help you eliminate manual tracking inefficiencies, improve visibility, and ensure every requirement is tested.
Managing test cases across multiple releases is a challenge for QA teams. Test Case Versioning changes the game by allowing teams to create, manage, and assign multiple test case versions. Learn how this feature helps QA teams streamline workflows and improve test execution reliability.
Automation metrics are more than just numbers - they offer valuable insights into test efficiency, defect detection, and overall software quality. In this guide, you'll find the key metrics that matter most for QA leads, from test coverage and execution time to flaky test rates and automation ROI.
Automation is key to efficient software testing, but managing tests at scale can be challenging. Xray simplifies this by integrating seamlessly with Selenium and Cucumber, offering centralized test management and advanced reporting to streamline testing and enhance traceability.
Software testing is evolving beyond defect identification to prioritize scalability, efficiency, user experience, and security. Explore the transformative trends empowering software testing in 2025.
Test design impacts quality, speed, and collaboration. With the Xray-YEST integration, you can transform your test requirements into smart test cases. Achieve 40% faster test design, streamlined manual and automated testing, and full visibility with centralized workflows in Jira.
Software quality has evolved over time. Initially, the focus was on finding and resolving defects prior to a product launch. As technology advanced, the emphasis shifted towards maintaining quality ac
While AI excels at automating repetitive tasks and analyzing data, humans contribute creativity, critical thinking, and the ability to navigate complex scenarios. This article delves into the strengths of both, exploring how they enhance software testing.
With AI, testers can identify potential problems earlier, streamline planning, and enhance test coverage, all while improving resource management. While the transition brings its own challenges, such as data quality and skill adaptation, AI's benefits are vast. Learn about it here.
This article highlights three skills: fast learning, critical thinking, and clear communication and links it to the Feynman Technique for accelerated learning. Fostering a culture of curiosity and collaboration is crucial for delivering top-notch quality in products.
To master exploratory testing, you'll need a strong foundation in crafting effective test scenarios, analyzing data, iterating quickly based on feedback, and identifying potential risks. Learn more with these 5 top exploratory testing books.
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