Systems | Development | Analytics | API | Testing

The first-ever Sembi Software Quality Pulse Report is based on nearly 4,000 responses from QA engineers, developers, security professionals, and engineering leaders worldwide. The findings paint a picture of an industry in motion—and a QA function that increasingly relies on tighter integration, thoughtful AI adoption, and better-connected workflows to keep up. Here's a look at some of the data that matters most for agile QA teams working inside Jira-native environments. TL;DR.

It happens to all of us. As software projects grow, parts of the production code we ship end up without a comprehensive test suite. When you take another look at the same area of code after a few months, it may be difficult to understand; even worse, there might be a bug, and we don't know where to begin fixing it. Modifying production code without tests is a major challenge.

CodePush is a great way to ship over-the-air (OTA) updates, avoid app store approval delays, and roll out changes cautiously. Even though App Center has closed down, there are many options available to get started with CodePush. But some of the default settings can create undesirable behaviors, leaving teams wrongly thinking CodePush causes a bad user experience.

Organizations are under pressure to feed data lakes and lakehouses with fresher data while keeping a tight lid on cloud spend. The problem is that most ingestion stacks weren’t designed for the real-time, high-volume workloads that power modern analytics and artificial intelligence (AI). They rely on layers of connectors, ETL jobs, and maintenance processes that quietly inflate both infrastructure and operational costs. Confluent’s Tableflow was built to change that equation.

We’re introducing enhanced visibility for streaming workload performance on Confluent Cloud, making it easier for developers and operators to understand, troubleshoot, and optimize real-time applications. As Apache Kafka has become the backbone of data streaming, many teams rely on Confluent Cloud for its scale, elasticity, and reduced operational burden.

For years, credit underwriting was pretty straightforward. Lenders looked at a few fixed factors like credit scores and income, to decide who was worthy of a loan. If you didn’t fit the criteria, you were simply rejected. It worked, but only to a point. This approach left out many people who were actually creditworthy and often missed subtle shifts in market stability.

Most AI analytics tools added a chatbot to a dashboard and called it intelligence. These eight actually change how fast your team goes from question to decision.

A VP of Marketing presents an AI-generated performance review on a Monday morning. The CAC numbers are clean. The trend lines are directional. The exec summary recommends a $200K budget reallocation from paid search to organic content. The CFO nods. The budget shift is approved before lunch. Two weeks later, an analyst spot-checks one figure against the source system. The number doesn’t exist anywhere in the connected data.

A guide to learning how MCP registries help govern AI agent-to-tool connectivity AI agents are only as capable as the tools they can reach. When an agent needs to query a database, file a support ticket, or pull data from a CRM, it has to find the right tool, authenticate, and invoke it — all at runtime. The Model Context Protocol (MCP) standardizes how agents communicate with these tools. But MCP alone does not answer a fundamental question: how does the agent know which tools exist?

Most hospitals are digitally enabled, but not digitally connected. Patient information exists across registration systems, EHRs, lab software, pharmacy tools, and billing platforms. Each system captures data, but none owns the full patient journey. Staff move between screens, re-enter information, and rely on manual coordination to keep workflows moving. This is the underlying reality behind the challenges hospitals face without a centralized HMS.