Systems | Development | Analytics | API | Testing

SmartBear participated in and won first prize at the ALMathon, the annual hackathon for SAP Partners. The challenge: Create innovative use cases to extend SAP Cloud ALM. So, we integrated Reflect, our AI-powered test automation product, into SAP Cloud ALM to enable users to automate their manual testing process.

Why do some QA teams consistently deliver reliable and high-quality software, while others toil to identify bugs and experience unstable releases? The real difference often is related to how easily the team is able to use software testing metrics to make measurable decisions. Often, the testing process turns out to be a routine checklist activity – run the tests, publish the results, and move on. However, without useful test metrics, the QA teams simply keep guessing.

Data integration logging is the process of recording events and activities during data movement across systems, ensuring accuracy, troubleshooting, and regulatory compliance. In complex environments like those of Netflix and Airbnb, logging is critical for system reliability and performance. However, challenges such as managing large log volumes, inconsistent formats, and compliance requirements make effective logging difficult.

Load testing, performance testing and stress testing are often mixed up, but in today’s CI/CD pipelines and production-grade engineering, they are solving completely different purposed. If you want to: Automate testing within CI/CD pipelines, such as with Keploy, JMeter, Locust, or k6 This guide discusses the difference like Performance testing, Load testing and Stress testing.

When it comes to handling security breaches, cloud and on-premise environments offer vastly different incident response approaches. Here's what you need to know: Cloud setups prioritize speed and automation. They reduce recovery times by up to 80% with tools like automated playbooks, real-time monitoring, and built-in redundancy. On-premise systems offer full control over hardware and data but rely heavily on manual processes, leading to 25% longer recovery times on average.

We’ll always remember 2025 as the year AI code assistants went big. Copilot, Cursor, Claude, Windsurf, whatever. Developers went from mistrusting these tools, to being expected to turn over much of their coding labor to them. Even if, according to an extensive Stack Overflow survey, only 3 percent of professional developers say they ‘highly trust’ AI coding tools.

After some testing and bug fixes, one common question always remains: how do teams make sure that those defects are truly resolved, and no new regressions creep in? That’s where retesting testing becomes vital. Retest testing forms a very important aspect of any QA cycle, ensuring that the reported defects are fixed and working correctly before the software moves to production. Without it, even simple patches can introduce silent issues into live environments.

For years, APIs have been designed with one primary audience in mind: developers. The focus has been on making APIs discoverable, consistent, and easy for humans to consume. But in the AI era, a new audience has arrived: AI agents. As detailed in SmartBear’s newly published AI-Enabled API Lifecycle Report, this shift demands that organizations rethink how APIs are designed, tested, and managed to serve both human developers and intelligent systems effectively.

As coding has become more autonomous, so has code-based testing. AI agents can now write functions, generate code-based tests, and validate logic in the same workflow. But the other half of the testing equation, the system-level validation of non-code-based testing, hasn’t kept up. That disconnect is becoming one of the most critical constraints in modern software delivery.