Software Testing Blog

Shipping an LLM without a proper evaluation strategy is a gamble most teams don’t realize they’re taking. 67% of organizations worldwide now run LLMs in production, but the majority still rely on LLM evaluation metrics designed for 2018-era machine translation or skip structured evaluation altogether. The result is predictable: hallucinations that make headlines, chatbots that give illegal advice, and model updates that silently break things no one notices until users start leaving.

Shipping your app to both Apple and Google stores sounds straightforward until your app sails through one review and gets bounced by the other for a rule you didn't know existed. Such things happen constantly, not because your team wasn't careful, but because Apple and Google operate with fundamentally different philosophies, and treating them as two flavors of the same process is where the trouble starts.

Buyers of any automotive products and services today go online to research models, compare trims, check financing scenarios, and read specs on their phones long before they commit to a purchase. It means that you might lose them at any stage of that research due to a minor bug.

You spend months building the game with sharp design and innovative mechanics. Your team is rightfully proud of their work, but then you submit it to the store and get rejected within 48 hours. The reason could be as trivial as a broken privacy policy link or a crash on an older iPhone model that nobody tested, something that could have been easily prevented through dedicated mobile game testing.

Your functional tests are green. Unit tests pass. You deploy on Friday. Monday morning, the support queue is on fire: the pricing page is broken on Safari, the checkout button hides behind a promo banner on mobile, and the settings modal clips on tablets.

If you develop Android apps, your compliance to-do list just got longer. A wave of US age verification laws is forcing Google Play to rethink how apps reach younger users — and your development roadmap needs to reflect that.

Most RAG failures don’t look like failures at first. The model sounds confident. The response reads well. But the retrieved context was wrong, or the answer drifted from the source entirely.

If your QA chatbot gives three different answers to the same question, users stop trusting it long before your funnel report catches up. That inconsistency is not a “quirk of generative AI,” it is a quality issue you can and should test.

If users need to put an extra effort to use your app, they won’t. Simple math. When buttons can’t be read by screen readers, or forms break at larger font sizes, people don’t report bugs. They uninstall.

Your mobile app is live. People are downloading it, using it daily, maybe even paying through it. But here's the uncomfortable question: how safe is it, really? This is where mobile application security testing tools come in, as they can help ensure you are 100% confident in your product.