Java Web Development (JSP/Servlets) Services

There’s no shortage of reporting tools, but a good number of them are either part of heavyweight BI systems or cloud services. Many line‑of‑business applications, however, just want a discreet, built‑in reporting option that can be customized. 

Having recently tested several Java‑based document generation tools and libraries, I thought a short, plain-spoken, and up-to-date review could be worth sharing. 

Nowadays, Java enterprise applications often default to Angular, React, or Vue for the frontend. But for this kind of application, the most natural UI framework already exists in the Java ecosystem: Jakarta Faces.

Modern Java enterprise applications tend to follow a familiar pattern: a Java backend exposing REST APIs and a JavaScript/TypeScript frontend built with some library like Angular, React, or Vue. This architecture has become so standard that we rarely question it.

Building generative AI applications in Java used to be a complex, boilerplate-heavy endeavor. You’d wrestle with raw HTTP clients, hand-craft JSON payloads, parse streaming responses, manage API keys, and stitch together observability, all before writing a single line of actual AI logic. Those days are over.

Genkit Java is an open-source framework that makes building AI-powered applications in Java as straightforward as defining a function. Pair it with Google’s Gemini models and Google Cloud Run, and you can go from zero to a production-deployed generative AI service in minutes, not days.

GitHub Copilot coding agent runs in an ephemeral GitHub Actions environment where it can build your code, run tests, and execute tools. By default, it uses the pre-installed Java version on the runner — but what if your project needs a specific version like JDK 25?

In this post, I'll show you how to configure Copilot coding agent's environment to use any Java version, including the latest JDK 25, ensuring that Copilot can successfully build and test your Java projects.

Data-driven testing, also known as parameterized testing, is a technique that uses a data table to drive test execution by defining inputs, expected outputs, and test environment settings. It separates test data from test logic. Rather than creating multiple test cases for different input values, a single test case is executed repeatedly using different data sets.

The TestNG framework provides the @DataProvider annotation, which supplies data sets to tests. There are multiple ways to perform data-driven API testing using TestNG’s @DataProvider. In this article, we will learn how to pass test data using an Object array.