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Testing state machines in cloud apps is vital for reliability, performance, and handling various conditions. Automated Python scripts mimic real-world use cases to expose issues, bugs, weaknesses, and timing problems. They also help optimize performance. The included asyncio and multiprocessing examples provide valuable insights into cloud app state machine behavior, empowering product teams to build stronger, more efficient apps.
Writing scripts for security tasks can sometimes cause SecOps teams some difficulty. StackSpot AI, an AI assistant for software development, helps by creating scripts faster and more securely. It simplifies script creation for non-developers, automates updates, and helps maintain consistent security configurations across teams. This improves efficiency and reduces errors in security operations.
Thorough testing of Kubernetes clusters is critical for any organization that values high-quality application delivery, resilience, and security. An untested Kubernetes cluster represents major risks for your organization. Therefore, a comprehensive Kubernetes testing strategy is not just good practice—it's essential for the success of your IT and development projects.
AI, AI, AI—it is everywhere. We all read this in the news, see it in politics, in our web traffic, and now it’s coming to our tools. Interestingly, testing has been identified as one of the most essential areas for AI as well as for the safety of the public. This article considers references to start you on your AI test journey, "classic AI" problem areas, and identifies possible concepts to use when testing AI. As usual, it comes down to a willingness to learn new things or apply historical ideas to advance your test career.
Generative AI is changing testing practices by automating the creation of test cases, adapting to software changes and improving test efficiency. This highlights the growing importance of artificial intelligence in improving test coverage and accuracy, making test automation even more adaptive and intelligent. It has the potential to change the way software is tested, ultimately leading to higher-quality software products.
The Maestro Automation Framework stands out as a robust open-source tool, offering a plethora of features beneficial to software development teams. While it boasts of many strengths, it is essential for organizations to be aware of its limitations. By understanding these limitations and adhering to best practices, teams can harness Maestro's capabilities to its fullest, ensuring efficient testing processes and the delivery of top-tier software products.
One tool that has come a long way in simplifying testing efforts through automation technology is Selenium. Read more to learn the benefits of web automation testing and why Selenium can be the ultimate choice for a solution that can aid the web automation testing processes for organizations of different sizes and industries.
Adopting a machine learning-driven self-healing technique in test automation can prevent flaky tests, reduce test failures, and save time on code maintenance. Self-healing is one of the essential factors for successfully performing continuous testing in the DevOps model.
Testers come from a wider range of backgrounds, and have complex multifaceted roles. People who test are not “just testers…” At present, many testers do not feel well-supported by their tools. As my research uncovered stories of frustration, fear, and anger, I realized the illusory role of usability in tool adoption and the importance of understanding who is using those tools.
Test automation can reach a point at which it is no longer supporting organizational goals. Martin Ivison examines four key causes for this unhealthy state and finds out that carefully chosen metrics and a holistic, adaptive, and risk-driven approach go a long way to prevent and remedy this problem.
If you are a manual tester and want to be a QA automation engineer, learn Java and programming via these 10 steps.
DevOps is the preferred methodology for software development companies looking to code, build, test, and deploy software as a continuous process. It is popular because it creates a fast-paced, results-oriented, collaborative environment that encourages cross-skilling and self-improvement.
In life and in test automation, a lot of things change as you mature—the challenges you face, the types of failures you experience, and the best ways to solve them. Let’s skip the “life lessons” and focus on the test automation angle here
Performance testing is an important procedure to be carried out before approving any software product for shipment. You’ve probably heard some horror stories from senior colleagues about a time when the system was shipped without any performance testing. So now, it is an essential part of your testing. There are various tools for implementing performance testing for non-GUI middleware systems, but there are times we don't have the liberty to choose from an existing set of tools for performance testing
Some people assume that exploratory testing is a task with low-effort thinking, where the tester simply goes through the application and sees what comes up. While we shouldn't discount doing just that, because sometimes it does reveal some interesting bugs, there are techniques and patterns that testers can follow when exploring an application. Let's look at a two-step process to use in exploratory testing.