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.

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.

As AI agents move from prototype to production, organizations face a growing paradox: how to give these agents enough access to unlock business value—without compromising privacy, compliance, or control. This isn’t just an integration problem. As soon as you map API layers or ask how a generative agent might retrieve sensitive customer records, the challenge becomes one of governance, scale, and trust.

At SmartBear, we recognize organizations that improve software quality by increasing clarity, alignment, and confidence across the development lifecycle with the Developer Visibility Award. For 2025, the award goes to Datalex, a leading airline e-commerce solutions provider. Datalex equips airlines with API-driven platforms that provide tools for driving revenue and profit as digital retailers.

Traditional synthetic data generation approaches, still called “Test Data Management” (TDM) by legacy vendor, were designed for a world where applications were monolithic, databases were the center of gravity and change happened slowly. The world looks a lot different now. Modern systems are distributed, often times event-driven, and increasingly powered by streaming data and AI agents. In this environment, batch-oriented synthetic data generation fails to capture how systems actually behave.

In Part 1 and Part 2 we explored why testing modern software is so difficult. Production data is the most valuable input for testing, but it’s locked away because it contains PII and sensitive context. Traditional Synthetic Data Generation (SDG) was built for batch databases, not streaming systems. And AI coding agents amplify every weakness in existing test strategies because they need current, realistic data or they generate buggy code based on outdated assumptions.