Systems | Development | Analytics | API | Testing

Manual testing is essential to quality assurance, but it doesn’t always scale with fast release cycles. Clicking through forms, checking user flows, and repeating the same regression tests before every release can quickly become a bottleneck. Automation testing takes repetitive checks off your QA team’s plate. Instead of manually checking the same flows again and again, teams use testing tools to run predefined tests automatically.

A hospital might have years of EHR data, ICU records, staffing logs, claims history, and diagnostic reports in different systems. Yet it may still miss signs of patient deterioration before an ICU escalation. This gap is why predictive analytics in healthcare has shifted from experimental AI projects to a key strategy in 2026. Now, healthcare organizations use predictive models to identify sepsis risk earlier.

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.