testing

Testers and developers often have a strained relationship. Each side has a certain level of expectations as to what the other side should know and do, while there is little understanding of the constraints, conditions, and requirements that the other team has to work within. But it does not have to be this way. A little effort in giving more specific and helpful feedback can go a long way toward improving attitudes.

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.

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.

Whole-team testing means the whole team understands and participates in testing, using testing education as a tool to support quality efforts. And to be able to support testing in a meaningful way, team members must experience how testing is done by professional testers. Understanding skilled testing can help non-testers realize what quality criteria should be there and what elements of a product contribute to great quality.