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.

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.

Many organizations are looking to expand their automation abilities by designing and developing test automation frameworks. However, we often abandon good coding practices in favor of working as fast as possible. We need to treat this project like any other application development project. Here are three of the most important clean coding practices to keep in mind in order to make a scalable test automation framework.

Lots of test automation efforts in agile software development fail, or at least do not maximize their potential. This article looks at two main reasons test automation may not live up to the expectations that testers and other stakeholders in the agile development process have, then outlines six steps to avoid falling into these traps. Here's how to succeed with test automation in an agile environment.