I'm sure you know this classic line from Jurassic Park:

“Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.” —Dr. Ian Malcolm, Jurassic Park

That quote was about cloning dinosaurs, but honestly, it also applies pretty well to a scenario I'm now facing: resurrecting long-dead source code.

Recently, I joined the team at Moderne, where I’ve been diving into the world of automated code remediation and transformation using OpenRewrite. It’s powerful tech that can scan large codebases and apply structured, deterministic changes like it actually understands the code (because it basically does).

I've been wanting to write some custom OpenRewrite recipes as a learning exercise, and I've been looking for a good idea to try out. This got me thinking: What kind of code would be fun (and maybe a little terrifying) to experiment on?

That’s when I remembered Help Desk Scheduler, the scheduling system I built for my senior project in college circa 2003. It was powered by Struts 1, backed by MySQL 4, and built with Java 1.2 with no build tool in sight.

What could possibly go wrong?

# Digging Up Fossils

The Help Desk Scheduler (HDS) was a handy tool I created while working at the Cal Poly ITS Help Desk. It was a web app that generated work schedules for staff and students based on user-defined rules. It let supervisors schedule student workers, manage shift swaps, and view schedules in various formats. It solved a real problem, and it actually worked!

Back then, frameworks like Struts 1 were still new and exciting, Tomcat 4 was the default server choice, and MySQL was all the rage. For this project, I built everything with raw javac commands in a batch file. No WAR file. No CI/CD. Just a folder full of class files and a dream.

Somehow, I had kept a copy of the source code, preserved like a mosquito in amber, complete with J2EE DNA. So I decided to see: could I bring this fossilized application back to life and use it as a playground for OpenRewrite?

# Reanimating the App

Getting it running again meant digging through ancient APIs, outdated assumptions, and a build process so flaky that I can hear Dennis Nedry now: “Uh uh uh, you didn’t say the magic word!”

Here’s what it took to get it running in 2025:

• Sticking with Tomcat 4 (v4.1.24 downloaded from the Apache archives)
• Using the oldest "supported" version of MySQL: 8.0.13 (plus a newer JDBC connector)
• Tweaking the database schema and JDBCRealm configuration to support MySQL 8
• Swapping out JavaMail SMTP for MailDev (good enough for these purposes)
• Dockerizing everything with docker-compose to make things easy to run (and portable)

After a weekend of tinkering and a few "I can't believe this is working" moments, I had the app running again in my browser, in all of its ugly, table-based, CSS-less glory.

Check out the Help Desk Scheduler GitHub repository.

# But... Why?

Let’s be clear: I have no plans to offer a commercial or even production-ready app. But having a working legacy app gives me a useful, safe, and fun sandbox for experimenting with modern code transformation using OpenRewrite.

Some experiments I’m thinking about trying on this codebase:

• Migrate from MySQL to PostgreSQL
• Upgrade to Java 21
• Replace Struts with Spring MVC (or Struts 2, or something else)
• Move hardcoded config to external properties
• Swap out the authentication layer
• Eventually maybe even refactor into something that resembles a modern Spring Boot app

Most of these are squarely in OpenRewrite’s wheelhouse, but I can also try for some stretch goals to give me a way to explore how far automation can take things vs. where manual intervention may still play a critical role.

# What’s Next

Now that I’ve got this prehistoric app running again, I plan to document my OpenRewrite experiments in future posts. I’ll explore what works and what breaks, and where automation helps or not when dealing with very old Java code.

But for now, I’m just happy that I got this dinosaur of a project running again. Not because I should, as a wise chaotician might say, but because I could. I hope the servlets don't bite!

It was 11 years ago (to the day, if you can believe that) that I started a new job after leaving my first job out of college. (Fun fact: that was also an 11 year run.) Since then, I’ve been charting my career journey in this space. It sure has been quite a ride, filled with diverse roles, inspiring leaders, and wildly different company cultures.

Most of my time has been spent inside large enterprise companies with tens of thousands of employees. In my most recent roles, I’ve even been focused on selling software and application platforms to enterprise customers, giving me a unique view from both sides of the table.

That said, I’ve also had a couple of stints in startup and startup-like environments with as few as 100 people. The contrast between those experiences and the enterprise world is stark. It reminds me of some movie quotes...

“There’s a difference between knowing the path and walking the path.” Enterprises tend to be structured. In fact, with rigid processes, strictly defined roles, and lots of layers, I’d say they are often too structured. By contrast, in startups you’ll find more fluid responsibilities with a frequent need to adapt on the fly.

“All we have to decide is what to do with the time that is given to us.” Startups move fast. Decisions happen quickly. Iterations happen faster. In a big company, on the other hand, getting anything done usually means wading through a frustrating swamp of cross-functional alignment meetings, approvals, and never-ending loops.

“The study of pressure and time.” Sure, enterprises come with an abundance of resources, but agility usually isn’t one of them. Startups might be resource-constrained, but this has a way of forcing creative thinking, building resilience, and leading to more innovative outcomes.

“Old and busted, new hotness.” Startups can build with the latest tools, trends, and tech from the ground up. Enterprises, meanwhile, are often tied to legacy systems and are forced to drag a lot of baggage along for the ride. It’s much harder to steer the ship into new waters.

While I have not spent the majority of my career in startups, I’ve loved the time that I have. I still vividly remember my first exposure to startup speed. A bug was discovered, and a fix was coded, tested, and pushed to production all within an hour. My mind was blown. That one moment taught me more than months in the enterprise, and I got to tap into skills I didn’t even know I had.

Eventually, though, I got sucked back into the enterprise machine, and I didn’t fully realize how much it had started to wear on me. The longer I stayed, the more my disillusionment grew as it chipped away at my energy and motivation to ultimately break me down.

Now, at last, I am building myself back up. I’m thrilled to say, I’m heading back into startup-land. Today, I’m joining Moderne as a Technical Marketing Lead. It checks so many boxes for me.

True Modernization. Moderne is tackling a challenge close to my heart: improving code quality and reducing technical debt at scale through automated refactoring. As a former product manager, I still have scars from punting on feature work so the team could upgrade dependencies, migrate to TypeScript, or swap logging libraries. The opportunity to improve developer productivity and enable tech stack liquidity, particularly for enterprise companies with massive code bases, is incredibly exciting.

Closer to Code. After years in infrastructure and application platforms, it feels good to get closer to where software actually gets written. I may be in a marketing role, but I’ll still get to frequently nerd out about parsers, visitor patterns, and Lossless Semantic Trees (LSTs) thanks to the magic of OpenRewrite, the open source project powering Moderne’s platform.

AI That Matters. The AI boom has been overwhelming, but Moderne isn’t just bolting on AI for buzz. They’re thoughtfully weaving it into the platform, using a hybrid approach to combine their rules-based system of deterministic recipes and balancing it with all that LLMs and machine learning brings to the table.

Broad Skill Application. I’ve always gravitated toward roles that blend technical expertise and depth with strength in soft skills like communication, collaboration, storytelling, and problem-solving. Moderne’s small and nimble team gives me the chance to wear multiple hats and contribute wherever I’m needed most.

People I Respect. I’m lucky to be joining a team full of folks I’ve admired for a while. It’s energizing to be surrounded by smart, driven people. Plus, there’s a strong Java foundation here that keeps me connected to my friends in the Spring community.

Remote First. The Moderne team is globally distributed, and I’ve been working remotely since before it was cool. While I certainly appreciate in-person meet-ups on occasion, async communication suits me just fine. I've been able to build trust through consistent delivery rather than relying on physical presence, and with today’s collaboration tools, it’s easy for remote teams to stay connected and effective.

As I step into this next chapter, I’m excited to help reshape how developers write and maintain software by making refactoring easier, faster, and smarter. Let’s go!

I know from my experience working for and with enterprise companies that keeping dozens or hundreds (or thousands!) of apps up to date is complicated. Much of my career in tech has been spent in and around the cloud-based platform and modern application development spaces in an attempt to help solve this problem for customers. But I also spent time as a product manager working directly with developers, so I’ve seen how even with automated CI/CD pipelines, modern app architectures, and robust app platforms, it ultimately comes down to effectively managing a code base and often tackling mountains of tech debt along the way. I remember having to spend precious sprint cycles on cleaning up and refactoring whole swaths of code instead of focusing on delivering features for end users.

I’ve also seen over the past many years how even the most successful moves to cloud can still lead to a lot of challenges when it comes to data migration. Plus, with the explosion of Internet-of-Things (IoT) devices, it’s getting more and more difficult to ship data off to the cloud for processing. It’s been fun to watch the trend towards edge computing to combat these obstacles, but of course, that brings its own set of challenges from a scaled management perspective. I remember working on this almost ten years ago with automated bare metal hardware deployments, but now there is even more to consider!

These are hardly solved problems, but thankfully, a few of my former colleagues have ended up at companies where they are addressing them with some very innovative solutions. In my career, I’ve been extremely lucky to meet and work with some truly smart people, and one of the perks of knowing so many sharp folks in tech is that just by following their career paths, I can keep up to date with a lot of industry trends and get exposed to technologies that are new to me. This is how I became aware of two open-source projects that I’ve recently been exploring...

# OpenRewrite

OpenRewrite is an open-source tool and framework for automated code refactoring that’s designed to help developers modernize, standardize, and secure their codebases. With all the tech debt out there among enterprise teams managing large Java projects in particular, OpenRewrite was born to work with Java, with seamless integration into build tools like Gradle and Maven. But it’s now being expanded to support other languages as well.

Using built-in, community, or custom recipes, OpenRewrite makes it easy to apply any changes across an entire codebase. This includes migrating or upgrading frameworks, applying security fixes, and imposing standards of style and consistency. The OpenRewrite project is maintained by Moderne, who also offers a commercial platform version that enables automated refactoring more efficiently and at scale.

# EVE (Edge Virtualization Engine)

EVE is a secure, open-source, immutable, lightweight, Linux-based operating system designed for edge deployments. It’s purpose-built to run on distributed edge compute and to provide a consistent system that works with a centralized controller to provide orchestration services and a standard API to help manage a fleet of nodes. Think about having to manage hundreds (or more!) of small-form-factor devices like Raspberry Pis, or NUCs that are running in all sorts of places across different sites.

With EVE-OS, devices can be pre-configured and shipped to remote locations to limit the need for on-site IT support. And with its Zero Trust security model, it protects against any bad actors who may easily gain access to these edge nodes that often live outside of the protection of a formal data center. Because it is hardware agnostic and supports VMs, containers, Kubernetes clusters, and virtual network functions, it also has the ability to run applications in a variety of formats. EVE-OS is developed by ZEDEDA specifically for edge computing environments and aims to solve some of these unique challenges around running services and applications on the edge. They also offer a commercial solution for more scalable orchestration, monitoring, and security.

# Let’s Build Something!

There isn’t exactly an obvious intersection of interest here, but bumping into these projects independently, right around the same time, got me thinking about how I could experiment with both of them and build something that balances practical OpenRewrite usage with something deployable via EVE-OS. This is what I came up with:

1. Write a very simple but somehow outdated Spring Boot REST app
2. Use OpenRewrite to refactor and “modernize” it
3. Containerize the resulting modern app
4. Deploy it to an EVE-OS “edge node” [locally]

Of course, this only scratches the surface of the potential that these technologies have, but it turned out to be a pretty fun exercise for getting started by just dipping my toe a bit into each of these areas. In case you’re interested in getting your feet wet too, I’ve summarized the steps I took below, including a link to the code I used.

# Refactoring a Simple Legacy Spring Application

As a developer, my Java knowledge is admittedly relatively surface level, but I do know enough to write a working REST controller. Here’s my simple class that just calls a basic endpoint and spits back out its JSON result:

package com.example;

import org.springframework.web.bind.annotation.*;
import org.springframework.web.client.RestTemplate;
import org.springframework.http.MediaType;

@RestController
public class HelloController {

    @RequestMapping(value = "/", method = RequestMethod.GET, produces=MediaType.APPLICATION_JSON_VALUE)
    public String hello() {
        System.out.println("Calling external service...");
        RestTemplate client = new RestTemplate();
        String response = client.getForObject("https://httpbin.org/get", String.class);
        return response;
    }
}

My Spring skills are pretty outdated, so I would say a refactor is most certainly in order. Accordingly, I figured I’d use OpenRewrite to accomplish three primary things when updating this code:

• Use the newer dedicated @GetMapping as an alternative for @RequestMapping
• Use the SLF4J Logger instead of the elementary System.out.println
• Upgrade from Spring Boot 2.x to 3.x
  • I didn’t show my pom.xml file here, but I used version 2.3 and will upgrade to 3.2

There are definitely other things I could choose to update. For example, I didn’t opt to write test cases in a test class, but if I had I could also have migrated from JUnit 4 to 5. I also saw some articles that suggested updating RestTemplate to RestClient or even the asynchronous WebClient. I didn’t find any recipes for this, though I could maybe tackle writing a custom one, but I left that out of scope for now. I’m satisfied with this limited example.

Since I first learned to build Spring apps with Maven, that’s what I opted to use here (but there is support for Gradle as well). The basic Maven plugin command to run for OpenRewrite is mvn rewrite:run, but that requires defining configuration and parameters in pom.xml. I wanted to keep everything dynamic and on the command line, so I passed everything in using the -D flag to define the properties:

$ mvn -U org.openrewrite.maven:rewrite-maven-plugin:run \
      -Drewrite.exportDatatables=true \
      -Drewrite.recipeArtifactCoordinates=org.openrewrite.recipe:rewrite-spring:RELEASE \
      -Drewrite.activeRecipes=\
        org.openrewrite.java.spring.boot3.UpgradeSpringBoot_3_2,\
        org.openrewrite.java.spring.NoRequestMappingAnnotation,\
        com.example.ReplaceSystemOutWithLogger

You can see the three active recipes that I passed in to perform the tasks I outlined above. The first two are recipes straight from the OpenRewrite catalog. The last one is too, sort of, but in order to pass it the necessary configuration options, I created a rewrite.yml file in the root of the project:

type: specs.openrewrite.org/v1beta/recipe
name: com.example.ReplaceSystemOutWithLogger
recipeList:
  - org.openrewrite.java.logging.SystemOutToLogging:
      addLogger: "True"
      loggingFramework: SLF4J
      level: info

This specifies what logging framework and log level to use. The active recipe references whatever name is used here, hence com.example.ReplaceSystemOutWithLogger.

And that’s it. Running the mvn command above does the magic, fixing the pom.xml file to reference Spring Boot 3.2 and updating the controller code as follows:

package com.example;

import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RestController;
import org.springframework.web.client.RestTemplate;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.http.MediaType;

@RestController
public class HelloController {
    private static final Logger logger = LoggerFactory.getLogger(HelloController.class);

    @GetMapping(value = "/", produces=MediaType.APPLICATION_JSON_VALUE)
    public String hello() {
        logger.info("Calling external service...");
        RestTemplate client = new RestTemplate();
        String response = client.getForObject("https://httpbin.org/get", String.class);
        return response;
    }
}

Notice @GetMapping has replaced @RequestMapping and the System.out.println has been moved to use a logger instead. The code still builds and runs fine, but now it’s up-to-date!

Here’s the repository with the full set of code: https://github.com/bryanfriedman/legacy-spring-app. It has the original code in main and the updated code on the refactor branch so you can use git diff main..refactor or your favorite diff tool to compare.

# Deploying the Refactored App to an EVE “Edge Node”

Now that we have a running, refactored app, let’s deploy it to “the edge.” But first, we need an EVE node. The easiest way to setup a virtual EVE node locally, it turns out, is to use a tool called Eden (clever) as a management harness for setting up and testing EVE. Eden will also help us create an open-source reference implementation of an LF-Edge API-compliant controller called Adam (also clever) which we will need to control the EVE node via its API. Eden is neat because it lets you deploy/delete/manage nodes running EVE, the Adam controller, and all the required virtual network orchestration between nodes. It also lets you execute tasks on the nodes via the controller.

To accomplish this setup, I mostly followed an EVE Tutorial that I found which was extremely helpful. It outlines the process of building and running Eden and establishing the EVE node and Adam controller. However, this tutorial was written for Linux, so I ran into a few snags that didn't work in my MacOS environment. As such, I ended up forking eden and tweaking a few minor things just to get it to work on my machine. This mostly involved getting the right qemu commands to make the environment run. You can see the specifics here in the forked repo. And of course, while the tutorial describes how to run a default nginx deployment to test things out, I obviously deployed this Spring app instead. I also discovered that I needed to specifically configure the port forwarding for the deployed pod in question in order to reach the app for testing.

Here are the slightly modified steps that I took:

Prerequisites

I installed all the following prerequisites if they weren't already installed, using brew where possible, or otherwise downloading and installing: make, qemu, go, docker, jq, git.

Prepare and Onboard EVE

1. Start required qemu containers:

$ docker run --rm --privileged multiarch/qemu-user-static --reset -p yes

2. Build Eden ( used my fork as indicated above):

$ git clone https://github.com/bryanfriedman/eden.git && cd eden/
$ make clean
$ make build-tests

3. Setup Eden configuration and prepare port 8080 for our app:

$ ./eden config add default
$ ./eden config set default --key eve.hostfwd --value '{"8080":"8080"}'
$ ./eden setup

4. Activate Eden:

$ tcsh
$ source ~/.eden/activate.cs

5. Check status, then onboard EVE:

$ ./eden status
$ ./eden eve onboard
$ ./eden status

Deploy the app to EVE

1. Deploy the Spring app from Docker Hub:

$ ./eden pod deploy --name=eve_spring docker://bryanfriedman/legacy-spring-app -p 8080:80

2. Wait for the pod to come up:

$ watch ./eden pod ps

3. Make sure it works:

$ curl http://localhost:8080

# Conclusion

After all this work, I’m not exactly an expert in automated refactoring or edge computing all of the sudden, but I do have a much better understanding of the technologies behind these concepts. While they might not seem particularly related, I can definitely see how a company might be interested in both of these paradigms as they look to modernize their apps at scale and potentially look at migrating them to run at the edge. With just these rudimentary examples, you can start to see the potential of the power they can provide at scale.