Systems | Development | Analytics | API | Testing

The stakes for AI and data privacy are high. Risks in non-production environments are increasing. Regulatory bodies enact new privacy regulations each year. And concerns are rising as the AI/ML boom continues. Businesses like yours must be aware of key AI data privacy challenges. Read on to learn what these challenges are and how to address them.

As 2026 planning kicks into high gear, your CFO may be putting every technology investment under the microscope, but the focus and pressure to deliver on AI Innovation has never been higher. For IT Leaders, the challenge is clear and straightforward. How do you strike a balance continuing to move your AI initiatives forward and maintaining a balanced budget in 2026?

Atlassian Rovo is the latest evolution of AI-powered knowledge discovery and workflow assistance, built by Atlassian to help teams turn scattered information into meaningful actions. Rovo works on three core dimensions: Rovo is available in all Atlassian products by default. Users have multiple methods to engage with the AI, each offering different levels of complexity and interaction.

‘API governance’ can sound like an intimidating concept. When I started as a solutions engineer at SmartBear in 2018, it took me a while to wrap my head around what exactly API governance entails and even longer to be able to explain it clearly and simply to our customers.

Have you ever ended a testing cycle and been disappointed to discover that a critical requirement had not been tested or had even been implemented? If so, you are not alone. It’s a common problem for software projects, especially when they become complicated. Identifying which requirement was tested and validating what was not tested seems like an insurmountable challenge, and that’s where a traceability matrix becomes important.

ISO 26262 is an international standard that addresses the safety of electrical and electronic systems within production vehicles. It encompasses everything from driver assistance and propulsion to infotainment systems (when such systems intersect with the functional safety of the vehicle) and vehicle dynamics control so that software and hardware components perform reliably under all conditions.

Let's talk about everyone's favorite part of mobile development: watching your tests fail just before you head home on a Friday.

In the field of statistics and data analysis, comparing means is a frequent endeavor—whether that means testing the effectiveness of a new drug or analyzing average student scores before and after a training program. In reaching these means accurately, analysts typically use two tests — the Paired t-test and the Unpaired t-test. Although both compare means, they are used in completely different situations. Let’s understand what they are, how they differ, and when to use each.

As we wrap this blog series, let’s take a step back. We’ve explored how agentic QA systems can support traditional software testing: one assistant, one workflow, one metric at a time. But what does it all add up to? The answer isn’t “more automation.” It’s more visibility, more alignment, and more confidence across the software delivery lifecycle.