As your QA team grows, manual testing can lose the ability to focus on likely problem areas and instead turn into an inefficient checkbox process. Using machine learning can bring back the insights of a small team of experienced testers. By defining certain scenarios, machine learning can determine the probability that a change has a serious defect, so you can evaluate risk and know where to focus your efforts.
When you’re designing a dashboard to track and display metrics, it is important to consider the needs and expectations of the users of the dashboard and the information that is available. There are several aspects to consider when creating a new dashboard in order to make it a useful tool. For a mnemonic device to help you easily remember the qualities that make a good dashboard, just remember the acronym “VITAL.”
In the era of agile and DevOps, release decisions need to be made rapidly—preferably, even automatically and instantaneously. Test results that focus solely on the number of test cases leave you with a huge blind spot. If you want fast, accurate assessments of the risks associated with promoting the latest release candidate to production, you need a new currency in testing: Risk coverage needs to replace test coverage.
With 2020 upon us, software development firms seeking to increase their agility are focusing more and more on aligning their testing approach with agile principles. Let’s look at seven of the key agile testing trends that will impact organizations most this year.
Teams everywhere are looking to speed up testing without sacrificing quality, so once again, some of the top articles last year were about continuous integration, machine learning, and—of course—how to best implement and use test automation. But readers were also interested in what they shouldn't be doing, with two high-ranking articles about test practices we should stop and a tool you may be misusing.
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.
There is much published about the data we generate to assess product quality. There is less discussion about the data testers generate for our own use that can help us improve our work—and even less is said about recommended practices for data collection. Test data collection, management, and use all call for upfront planning and ongoing maintenance. Here's how testers can improve these practices.
It's easy to make simple mistakes in data analysis. But these little mistakes can result in rework, errors, and—in the worst case—incorrect conclusions that lead you down the wrong path. Making small process changes can help you steer clear of these mistakes and end up having a real impact, both in the amount of time you spend and in your results. Here are some tips for avoiding rookie mistakes in data analytics.
When people do not have good luck with automation, it is hardly ever because of the tool being used, but almost always because of the wrong automation strategy, wrong expectations, and wrong adoption of automation. Automation tools only answer the “how” of automation, while having an automation strategy gives answers to who, where, when, what, and why. Here's why it's so important to have a test automation strategy.
Selenium has widespread adoption as a test automation tool, but it comes with some challenges. We talked to some experts in the test automation industry about Selenium’s reign as the tool of choice for UI testing, whether that crown is warranted, and what they think is important for teams to focus on when it comes to their test automation efforts. Then, Parasoft talks about how teams can solve UI testing challenges and make Selenium more maintainable with its new product, Parasoft Selenic.