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The contemporary mobile ecosystem is incredibly complicated. Applications today are not standalone anymore; they are dynamic, heavy in features, and constantly communicating with cloud solutions, wearables, and IoT devices. Although the use of traditional test automation has contributed to enabling engineering teams to remain in step with agile delivery, the sheer number of fragmented devices and continually changing user interfaces has revealed the limitations associated with it.

Contents AI is now embedded across the entire software development lifecycle. Developers use it to generate code. Product managers use it to prototype features. Teams use it to move from idea to deployment faster than ever. Code moves faster. Features ship more frequently. Iteration cycles shrink. Across industries, companies that embrace this speed have a distinct competitive advantage. But in highly regulated industries, including financial services, speed can’t come at the cost of quality.

According to Perforce's 2026 State of DevOps report, there is a direct correlation between DevOps maturity and AI success. In a highly mature DevOps environment, AI accelerates innovation, optimizes workflows, and enhances security. In an immature environment, it scales chaos, multiplies risk, and inflates costs. So, before we ask ourselves how to make the most of our AI solutions, we must assess if our foundational processes are prepared for the challenge ahead.

Your AI chatbot might give a customer the wrong price. A RAG-based support agent might cite a document that doesn’t exist. An AI coding assistant might suggest code with a security problem. These issues are common for teams releasing LLM features without proper testing. The reality is that many teams using GPT, Claude, or Gemini don’t have a strong testing strategy. They usually do a few manual checks or simple prompt tests and assume it’s enough.

Over the last year, at Testlio, we have upskilled more than 600 testers in our global community to test AI-powered applications.

February introduces practical improvements across Test management, TestCloud, and TrueTest that help teams move faster while maintaining better control over testing operations. Here's what we have released.

Recent outages tied to AI-assisted code changes have pushed companies into a corner. After several incidents with massive “blast radius” impacts, organizations like Amazon introduced stricter controls—mandating that senior engineers manually review all AI-generated code before it hits production. That response makes sense on paper, but it exposes a fatal flaw in the modern development pipeline.

Regression testing is a software testing process that ensures your existing features, designs, and dependencies continue to work as expected after changes or updates are made to your codebase. It detects unintended bugs or breaks introduced by modifications like new features, bug fixes, or configuration changes. Each new change introduces a risk of breaking existing functionality, potentially causing shipping delays or launch postponements.

A $25 billion Fortune 500 medical device company, Stryker, was targeted by an Iran-linked hacker group that claimed to have wiped over 200,000 servers, mobile devices, and other systems, forcing the company to shut down offices in 79 countries. The medical technology industry has been hit hard by this huge problem. It's a stark warning that even the largest names in the business world can be hit by clever wiper malware.