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5 Reasons Enterprise Test Automation Is So Challenging Most organizations understand that test automation is essential for modern application delivery processes. They’re just not sure how to make it a reality in an enterprise environment without exorbitant overhead and massive disruption. Enterprise organizations typically achieve small victories, but the process ultimately decays due to challenges in five main areas. Understanding these challenges will help us overcome them. |
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Fitting In Regression Testing by Shifting QA Left Fixing a bug in one area of the software may break something in another area. To detect whether defects have been introduced, we need to perform regression testing—executing certain test cases again to see whether a change has affected other existing features. But how do you make time for another testing cycle prior to every production release? You need to get QA involved earlier in the software development lifecycle. |
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A Tester's Role in AIOps “AIOps” stands for “artificial intelligence in IT operations,” or using machine learning and data science to solve IT problems. AI can help with many IT functions, including detecting and remediating outages, monitoring availability and performance, and IT service management. Like with DevOps, a tester plays an important part with AIOps—they just have to determine what that is. |
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How Continuous Testing Is Done in DevOps DevOps does speed up your processes and make them more efficient, but companies must focus on quality as well as speed. QA should not live outside the DevOps environment; it should be a fundamental part. If your DevOps ambitions have started with only the development and operations teams, it’s not too late to loop in testing. You must integrate QA into the lifecycle in order to truly achieve DevOps benefits. |
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Applying Data Analytics to Test Automation Testers gather lots of metrics about defect count, test case execution classification, and test velocity—but this information doesn't necessarily answer questions around product quality or how much money test efforts have saved. Testers can better deliver business value by combining test automation with regression analysis, and using visual analytics tools to process the data and see what patterns emerge. |
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Embedding Security in a DevOps World Faster DevOps processes also create new challenges. It was difficult enough to add security into a traditional waterfall software development lifecycle with monthly or quarterly releases, but now software updates are released several times a day! What can developers do to build and maintain more secure applications? Here are some ways to encourage better security practices throughout the DevOps lifecycle. |
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Bringing Continuous Testing to Your Organization Continuous testing means all your tests are executing all the time, providing continuous feedback into the quality and health of your applications. In order to achieve continuous testing, you must first adopt the right test automation strategy. Understanding how to bring in all different types of test automation practices as efficiently as possible enables you to get started down the path of continuous testing. |
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Infrastructure as Code: The Foundation of Effective DevOps The absence of versioned infrastructure as code (IaC) and automated provisioning undermines one of the most important benefits of DevOps: the ability to version, manage, and control the servers and networking required to run software applications in development, testing, and production. Automating infrastructure setup and continuous monitoring helps keep system environments stable and less susceptible to outages. |
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A Simpler Way of Using Machine Learning to Shift Testing Left The advantages of shifting left and testing as early as possible are obvious. But as you automate more testing, the test suite grows larger and larger, and it takes longer and longer to run. Instead, just automate the process of finding the right set of tests to run. The key to that is machine learning. This isn't AI bots finding bugs autonomously without creating tests; this is a different way to use machine learning, and it’s far simpler. |
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Inverting the Test Automation Pyramid A growing company was tasked to develop a test automation program from scratch, change its coding practices, and build a continuous testing toolchain. Martin Ivison details how they did it, including realizing that implementing the traditional test pyramid wasn't going to work—it would have to be turned upside down. They found out that small is beautiful, cheap is good, and cultural change matters. |
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