The standard test automation toolkit easily completes web and mobile automation, but it fails to detect elements on desktop and mobile content-based applications. Computer vision (CV) replicates the human eye using deep learning technology and can determine objects in pictures, which helps machines orient in space and perform repetitive detection tasks. Let's see how CV can help automate the testing of a much wider software product list.

QA and information security use different methods to approach the same goals. When both groups work together, they can make a greater impact on the security of our products. Here's how the QA team can collaborate with infosec to implement strong security standards, prioritize what to test, and obtain quicker feedback on processes, ultimately seeing fewer production incidents related to security.

One key benefit of metrics is that they can be measured using a standard process; we can explain the numbers, and leadership can understand what that means. The downside is that it is only a measurement, so issues can easily hide until they become problems, and great work can also go unrepresented. Sporting events are a great example: The end score tells you who won, but not the details of the game. We need to look deeper.

Many software testers are lamenting the impending demise of their jobs thanks to artificial intelligence. But Jon Hagar thinks there's no need to panic just yet. Here, he details some capabilities he's seen in AI, relates how these can be used in software testing, and explains why he thinks most people don't have to worry—although he also explains who should! As usual, it comes down to a willingness to learn new things.