Software Testing Blog

Seven generative AI use cases in software testing, ranked by return. What each one saves, what it needs to work, and where human QA still has to step in.

Compatibility testing tools priced against four real workloads. BrowserStack, LambdaTest and Sauce Labs 2026 list prices compared, plus when to outsource.

Apple's App Store AI data sharing guidelines now require explicit disclosure and consent. See what changed, what Google added, and how to check compliance.

Google Play app rejected in 2026? Five dated policy waves and a live-catalog re-scan changed what gets flagged. Every notice decoded, and the lane that catches it.

Ring, Arlo, Wyze, eufy, Aqara, and Tapo all build genuinely fantastic and innovative security systems. The cameras are sharp, the sensors fire fast, and the locks feel solid in the hand. Yet owners of every one of them keep reporting recurring issues, and they rarely lie with the devices. Smart home security problems most often hide in software, particularly within complex connectivity systems.

Every week our team bug crawls a popular AI app. Across 20 of them, one pattern held: the model rarely fails. The bugs live in the software wrapped around it, from prompt injection to lost data to small trust-breaking mistakes.

An AI agent cancelled a stranger's gym booking to jump the waitlist, and the flaw it walked through, broken access control, is probably in your product too.

Today, there are some truly amazing smart lawn mowers from Husqvarna, Mammotion, Segway, and Worx, to name a few. Just seeing those small but oh-so-useful robots moving around brings home the fact that we already live in the future. However, while today's tech allows the device to read the yard, step around the garden hose, and tuck itself back onto the charger when it's finished, it’s still far from perfect. As a result, many of those fantastic manufacturers don’t see their sales numbers climbing as quickly or as high as they should.

Ask a company's AI assistant to finish one short sentence, and it might hand over the private instructions its own developers wrote to keep it in line. Our QA engineers tried exactly that on a popular meeting assistant app, and it complied within seconds. That’s only one of the successful prompt injection examples we discovered while testing various AI-powered products. The model powering the product was fine in itself, but the app around it could not tell a hostile instruction from an ordinary request. That weakness sits at the top of every serious list of risks for AI-powered software.