Jetbrains on JAVAPRO International

Maintaining the IntelliJ “Save Actions” Plugin: A Community Effort to Keep Development Tools Up to Date

Wed, 12 Nov 2025 12:00:01 +0000

For many developers working with IntelliJ IDEA, the “Save Actions” plugin has long been a trusted companion for maintaining consistent and clean codebases. By automatically performing configurable actions on file save—such as optimizing imports, reformatting code, rearranging members, or applying small quick fixes—the plugin helps ensure that projects stay uniform across entire teams.

After the original development of “Save Actions” came to a halt several years ago, the plugin risked falling out of compatibility with newer IntelliJ versions. To prevent this, XDEV Software stepped in as the dedicated maintainer and continues to provide updates and maintenance releases through the JetBrains Marketplace. The goal has remained simple: to keep the plugin functional, compatible, and stable for the growing developer community that relies on it.

Japo: The Ultimate Search Engine for Java Annotations

Thu, 21 Aug 2025 07:00:01 +0000

In the evolving landscape of Java development, annotations play a critical role in simplifying code, enhancing readability, and enabling powerful framework capabilities. Yet, finding detailed, accurate, and consolidated information about Java annotations has long been a challenge for developers. JAPO (Java Annotations Repo) is the game-changing solution addressing this gap. Offering a feature-rich search engine, JAPO enables Java developers to explore annotations, Maven artifacts, and much more with unparalleled ease.

Code Reviews with AI: a Developer Guide

Wed, 30 Jul 2025 07:00:01 +0000

Code reviews are a cornerstone of software development. They’re where we share knowledge, catch bugs early, and ensure our code meets the highest standards.

But let’s be honest…

Traditional code reviews can be time-consuming and tedious and sometimes even miss subtle yet critical issues. Enter the age of AI-powered code review, a game-changer that addresses these challenges and elevates code quality to new heights.

This article dives into the common pitfalls of code reviews and explores how AI tools can revolutionize each phase of the development lifecycle.

I will discuss the use and impact of AI on the different phases of the SDLC from the 2 main perspectives of the developer and the reviewer. I will also talk specifically about the use of AI in the Code Review and how to implement it productively. I will provide examples of tools used in the different phases: Github Copilot, SonarQube, Qodo, and IntelliJ.

Are AI Coding tools worth it - RESULTS

Wed, 30 Oct 2024 12:32:45 +0000

Background

In Part 1 , I examined the value of AI tools for developers, especially those that help them create or understand code. The tools selected are all free to use and a mix of general-purpose ones and those that declare they are specifically for coding purposes.

In Part 2, I examined these tools’ ability to find bugs and weaknesses. The results were spotty and not particularly useful or time-saving.

In this part, we’ll look at the results from asking these tools to create JUnit tests for the code and decide who wins (and why).

Are AI Coding tools worth it - Part 2

Wed, 23 Oct 2024 16:04:46 +0000

In Part 1 , I started to examine the value of AI tools for developers, especially those that help them create or understand code. The tools selected are all free to use and are a mix of general-purpose ones and those that declare they are specifically for coding purposes

Tools being evaluated

1: ChatGPT https://chatgpt.com/

2: Claude https://claude.ai/

3: Amazon Q Developer https://aws.amazon.com/q/developer/

4: Codeium https://codeium.com/

5: JetBrains AI Assistant https://www.jetbrains.com/ai/

I asked each tool three questions

“Generate the main Javadoc to describe this class.”
“Assess the class for bugs and coding weaknesses and provide corrections or guidance.”
” Generate Junit test cases to provide around 90% code coverage -especially for edge conditions etc.”

Results so FAR

In Part 1, we saw that they could all generate Javadoc, but none could do it particularly well. ChatGPT came off best as providing the most comprehensive response, but all the tools were prone to hallucinations that made the result misleading.

Are AI coding tools worth it? - part 1

Thu, 17 Oct 2024 10:00:00 +0000

This is the start of a few posts about AI coding tools. This one sets the scene and gives a taste of what I found. In later posts I’ll show more of what I got from using them as is. , explore what the sweet spots might be and end with a harder look at getting value out of them. If you want to skip to the end go here

A long time ago, in a company far, far away…

In the very early 90’s I had the, well, honour is not quite the word I want, the experience may be, to use a prototype of one of the first viable commercial speech-to-text and speech-to-control tools. I used it at work; I used it at home. It was a disaster in all ways. In the first go-around, the training for learning your voice was arduous, literally painful, and demanding. In the second prototype, it would often pick up the sound of the hard disk on the computer and interpret it as text, to much hilarity for all.