Systems | Development | Analytics | API | Testing

In software development, load testing plays a critical role in ensuring that applications perform optimally under any imaginable load condition. To do this, developers subject applications to several types of load tests, including scalability, spike, endurance, and stress testing. The ultimate goal of these performance tests is to pinpoint potential bottlenecks and ensure the reliability of the overall system where the software application runs before reaching production.

Runtime validation does not replace static analysis. They solve different problems. Static analysis catches structural defects in code before it runs. Runtime validation catches behavioral failures by testing code against real production traffic. Enterprise teams adopting AI coding tools need both layers because AI-generated code introduces a new class of defects that neither layer catches alone.

Discover how AI helps continuously test games 24/7, catch bugs earlier, and eliminate dev crunch.

Struggling with flaky tests and slow automation? Explore the best AI-powered testing tools of 2026 — and why Sauce Labs stands out.

Agentic Test Automation for Tosca revolutionizes software testing. Using only natural language prompts, it automatically generates complete, executable test cases — allowing QA teams to keep pace with modern AI-driven development. This latest update expands support for new enterprise technologies and uses Tosca’s automation engine to become even more powerful. Enterprise customers can now create complex, end-to-end tests that are built and supported by Tosca’s proven technology.

Modern software teams rely heavily on automation to maintain speed and quality. But as systems grow more complex, one question becomes increasingly important: Where should automation live? The answer isn’t about tools, it’s about structure. And without a clear structure, even well-intentioned automation efforts can create more friction than confidence. Let’s explore how to approach automation strategically, starting with why the choice of testing layer matters.

AI test automation frameworks are transforming how teams build, execute, and maintain test suites by embedding intelligence directly into the testing workflow. Start small with a pilot framework implementation, prove ROI on a single project, then scale AI testing capabilities across your organization. Building an AI test automation framework requires more than bolting AI features onto existing test suites.

One of the most common infrastructure changes Java developers and operators are dealing with today is the migration from Oracle Java to OpenJDK. The reason is the licensing changes made by Oracle and the maturity of the OpenJDK distributions. The migration process is quite simple: replace the JDK, recompile the code, and redeploy the application. However, the differences between the two runtimes can lead to unexpected issues that are not caught by unit tests.

In the highly dynamic environment of modern engineering, an appropriate strategy for API quality is more important than ever. We are pleased to announce that Speedscale has been named in the latest “Market Guide for API and MCP Testing Tools” report from Gartner. As software development is shifting towards complex distributed architectures, integrating Model Context Protocol (MCP) for AI-based workflows, the need for realistic testing has never been higher.