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Software Quality Engineering
DevOps transforms testing from finding all bugs to prioritizing critical ones. Risk assessment helps testers focus on high-impact areas. Testing can be pre-release (critical issues), post-release (resolvable issues), or out-of-scope (low-impact). Testing should align with business goals and user stories. Data science helps measure and improve software quality.
For developers in privacy-centric fields, innovation goes hand-in-hand with responsibility. One security breach can shatter trust, expose sensitive data, and result in large mandatory fines. By integrating security testing throughout development, vulnerabilities are caught and addressed early, saving time.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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?
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.
All around us, we are seeing a growing trend of artificial intelligence (AI) being implemented in every aspect of our lives—from self-driving cars to intelligent chatbots. But what about the world of DevOps? Has AI come to play a role here as well? The growing role of AI in DevOps has unearthed some key benefits it can bring to many DevOps workflows.
The approach to test via comparison of multiple API responses between production and test code versions is very effective and produces the required results, release over release. Improvements and changes, however, are needed to address changing needs. This is true for most if not all tech solutions; the economics principle ‘Law of Diminishing Marginal Utility’ also applies to software. A tech solution that excited stakeholders when first introduced could become stale very soon. A revamp or a new solution is needed to match evolving expectations.
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.
Whether to use Cucumber and BDD (Behavior Driven Development) is an old topic and a lot has been shared regarding this already. However I believe many times it’s just the high level technical reasons that make it to the forefront of the argument on how effective it is, and I want to address the topic from a holistic point of view and not just technical.
The concept for development teams in a scrum environment is to be self-organizing, basically managing themselves and holding each other accountable. This poses the question: What do QA managers do with their time? For me, it’s always been about building the right culture—respecting those under you just as much as you respect those above you. It is about finding a way to manage your team without being directly involved with them.
Business owners and developers must keep in mind that modern users can be very picky and demanding, and they must do their best to meet all their users' expectations. The best way to solve this issue is to perform usability testing regularly.
Rapid change and adoption of new ideas are attributes we readily assign to engineering and high-tech. What was a novelty and special last year is old news today. And yet… have you ever heard of Metamorphic Testing?
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.
When author Zane Roett began a new Senior Manager test role in a new organization in 2019, he found that it became an important task to create and lead the development of a software test engineering professional community of practice (TCoP).