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Embracing Quality: The Integral Role of Software Testing in Development[article] Software testing is a critical phase in software development that ensures high-quality, reliable software. It involves a comprehensive range of activities to evaluate every aspect of a software product, from unit testing to continuous testing. By identifying and addressing issues early on, software testing helps to deliver software that meets user expectations and functions seamlessly. This not only benefits users but also fosters innovation and trust in technology. |
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Sponsored Content—SecOps: Streamlining Security with Script Standardization and Automation[article] 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. |
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Artificial Intelligence for Object Detection and Its Metadata[article] Deep learning, particularly Convolutional Neural Networks (CNNs), has transformed object detection in computer vision. These AI models excel at identifying and pinpointing objects in images and videos with high accuracy. However, integrating metadata like object class, location, and time with AI object detection unlocks even greater potential. This enriched data provides valuable insights for applications in autonomous vehicles, healthcare, retail, and manufacturing. |
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A Comprehensive Guide to Responsive Testing[article] Responsive testing ensures websites look and function well across devices by testing layout, functionality, and performance on various screen sizes and browsers. It's crucial for consistent user experience, navigation, image display, and website robustness. Testing methods include browser developer tools, real devices, usability testing, and performance testing. Common challenges include outdated browsers, device-specific behaviors, and network speeds. AI, Machine Learning, and Real User Monitoring are emerging trends in responsive testing. |
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Don’t Let Load Testing Lead You Astray[article] While traditional, "closed system" models might seem familiar, they miss the unpredictable nature of real user arrivals. This article highlights how open system models, which mimic real-world user behavior, expose performance bottlenecks at high load that closed models can't catch. Remember, arrival rates, not virtual user counts, are the key to unlocking accurate load testing results. |
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Testing Kubernetes Clusters: A Practical Guide[article] 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. |
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Real-Time Data Streaming Using Kafka[article] Kafka testing emerges as a linchpin in the construction of robust, reliable data streaming applications. Whether you’re scrutinizing producers, consumers, or the entire data conduit, the bedrock of a well-structured testing strategy proves instrumental in ensuring the optimal performance of your Kafka-driven system in production. |
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AI/ML Testing and Your Test Future (Part 2)[article] 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. |
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Generative AI—Is It the Catalyst for Evolution in Test Automation?[article] 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. |
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Discover YAML Based Test Automation Framework with Maestro[article] 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. |
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Examining the Impact of AI on Software Testing: A Conversation with Jeff Payne and Jason Arbon[interview]
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In the software community, the emergence of AI has stoked thoughts of both possibility and concern about its impact. For software testers, the critical questions swirling around AI are: “What is the future of testing in a world of AI? Will testers become obsolete?” Coveros CEO Jeff Payne talked with testing and AI expert and CEO of TestersAI, Jason Arbon, to find out what the emergence of AI means for the testing and software community. |
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Exploratory Testing: Why Is It Not Ideal for Agile Projects?[article] As the Agile environment has efficient principles that allow quick responses to changes and the ability to deal with uncertainty, exploratory testing may seem like a perfect match for such projects. However, this is only partially true. In reality, diverse reasons impede its usage in Agile projects, and companies should take them into account before designing QA strategies. |
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Benefits of Using Static Code Analysis Tools for Software Testing[article] Static code analysis tools offer a range of benefits for software testing. By identifying potential issues early on in the development process, these tools can help improve the quality and reliability of software, the codebase’s maintainability, and software security by identifying potential vulnerabilities and security issues. |
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The Rise and Fall of Protractor, and Why You Should Stop Using It[article] Protractor has been a popular choice for writing end-to-end (E2E) browser tests over the past decade. However, Protractor is being removed from the Angular project as of Angular 15 due to a loss of dedicated developers and the rise of better testing frameworks. Based on this state of affairs, what should developers working with Protractor do, and what tool choices do developers have going forward? |
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Uplifting QA Teams Through Metrics[article] As software products and services are becoming more complex, it requires more comprehensive testing for identifying the defects and pursuing the necessary fixes before it becomes available for public release. The QA process needs to be planned out and monitored so that it can be successful. The most effective way to track the efficacy of QA activities is to use well-thought-through metrics. |
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