Best practices for test automation emphasize reliability, portability, reusability, readability, maintainability, and more. But how can your existing automated test suite adopt these qualities? Should you address these issues with your current tests, or create an entirely new set of tests? Here are some questions that will help you determine if your test automation maintenance program is operating as it should be.

On a new project, we often lack the luxury of having a subject matter expert available to answer our questions. When that’s the case, we have to become our own SME. Here are a few key methods from the writings and presentations of experts in various fields that deal with information gathering and rapid learning. You can easily use these methods, right now, to quickly gain the knowledge you need in order to move forward.

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.

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.