Java Web Development (JSP/Servlets) Services

In this article, we will walk you through how to conduct a load test and analyze the results using Java Maven technology. We'll covering everything from launching the test to generating informative graphs and tables.

For this demonstration, we'll utilize various files, including Project Object Model (POM) files, JMeters scripts, and CSV data, from the jpetstore_loadtesting_dzone project available on GitHub. This will help illustrate the steps involved and the functionality of the necessary plugins and tools. You can find the project here: https://github.com/vdaburon/jpetstore_loadtesting_dzone.

When engineering teams modernize Java applications, the shift from JDK 8 to newer Long-Term Support (LTS) versions, such as JDK 11, 17, and soon 21, might seem straightforward at first. Since Java maintains backward compatibility, it's easy to assume that the runtime behavior will remain largely unchanged. However, that's far from reality.

In 2025, our team completed a major modernization initiative to migrate all of our Java microservices from JDK 8 to JDK 17. The development and QA phases went smoothly, with no major issues arising. But within hours of deploying to production, we faced a complete system breakdown.

Within the discipline of software engineering, practitioners are frequently encumbered by the monotonous ritual of initializing identical project scaffolds — configuring dependencies, establishing directory hierarchies, and reproducing boilerplate code prior to engaging in substantive problem‑solving. Although indispensable, such preliminary tasks are inherently repetitive, susceptible to human error, and inimical to efficiency. 

Maven, a cornerstone of the Java build ecosystem, furnishes an elegant mechanism to mitigate this redundancy through the construct of archetypes. An archetype functions as a canonical blueprint, enabling the instantaneous generation of standardized project structures aligned with organizational conventions. By engineering bespoke archetypes, development teams can institutionalize consistency, accelerate delivery, and reallocate intellectual effort toward innovation rather than procedural repetition.

REST Assured is a popular API test automation framework in Java. Software teams widely use it for efficiently validating RESTful web services with minimal setup. It simplifies the process of sending requests, verifying responses, and handling JSON or XML paåyloads.

With its rich syntax and integration support for tools like TestNG and Maven, REST Assured enables robust, maintainable, and scalable API testing.

Retrieval-augmented generation (RAG) is quickly becoming one of the most powerful design patterns for AI applications. It bridges the gap between general-purpose large language models (LLMs) and your specific enterprise data. In this article, we’ll walk through how to build a complete RAG pipeline in Java using Spring Boot, Vertex AI’s Gemini embeddings, Apache PDFBox, and BigQuery Vector Search.

You will see how to do the following, wrapped in a Spring Boot app with a simple web UI: