“If It Ain’t Broke, Don’t Fix It”… but Legacy Technologies?

It’s time to talk about legacy technologies but we’re not talking about the JS framework that was released one month ago, we’re talking about real legacy projects, really old-school things like this.

There are a bunch of projects out there still using some old technologies, some of those doing important things like banking, medical services, governmental services, and many more, let’s take a look at how popular they have been in the last 12 months. Here is a comparison of the Google search by some of these technologies:

Popularity = Functionality

Obviously to be popular doesn’t mean that it will work better. Still, in talking about building software it’s something to keep in mind, the new generation of developers probably will not be interested in learning this old-school stuff, and that will reduce the number of developers available to build new features, create new integrations with new services or new technology, maintaining software and also a community to support and maybe answer to your questions:

So AI is the answer?

This is the year of AI, if you go to youtube and take a look at the conference of the big tech companies you will hear “AI” too many times and all the new features have something of it in them. And maybe this could be the best approach right now or not? We don’t want to turn this into a monologue let’s ask ChatGPT:

“The conversion of legacy programming projects involves more than just translating lines of code. It requires a deep understanding of the underlying business logic, architecture, and specific requirements of the original system. While AI algorithms can excel in recognizing patterns, they may struggle with comprehending the intricacies and nuances of legacy codebases. Moreover, legacy systems often lack comprehensive documentation, making it challenging for AI models to accurately capture the full context and accurately translate the code.

In conclusion, while AI presents promising capabilities for converting legacy programming projects to new languages, it is crucial to consider its limitations and the complexity of the task at hand. The ideal approach may involve a combination of AI-assisted tools, human expertise, and robust testing methodologies to ensure the accuracy and integrity of the converted code. Ultimately, a thoughtful evaluation of project requirements, available resources, and the expertise of the development team should guide the decision-making process when choosing the most suitable tool for legacy code conversion.”

So as chatGPT said this is a combination of skills, tools, and experience, this will not be an easy process. We have some tools like Ispirer Toolkit, to test and share with you some approaches that will help you to measure this goal, keep posted and be patient because together we will try to figure out how to do it.

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