testing

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.