RockIT

In the previous post, we saw the limitations of AI developer tools. Yes, they are not perfect. However, they are still able to improve our productivity significantly.

This time, we’ll dive into how we can effectively use them to achieve a better development experience, higher-quality output, and faster delivery.

In the world of software development, understanding software behavior is crucial. It’s not just about writing code; it involves various steps and artifacts. In this post, we’ll dive into the different ways to describe what a software product does, showing how they all connect to depict software behavior.

With the rise of ChatGPT, Bard Gemini, GitHub Copilot, Devin, and other AI tools¹, developers started to fear that AI tooling would replace them. Even though their capabilities are indeed impressive, I don’t fear our jobs will go away in 2024.

We’ve previously dived into the complexities and maintenance challenges of switch-case and saw a refactoring technique for simple scenarios.

Today, we’ll raise the bar a bit and focus on situations when our type code affects behavior. We’ll use pets to demonstrate the concepts.

In the previous part, we had an overview of why switch-case could be hard to maintain. This part will focus on the simplest scenario: when type-code only affects data, not behavior. We’ll do this by modeling a pizzeria.

In a nutshell: switch-case makes code harder to maintain. We’ll understand the reasons to eliminate it and see when its usage is justified.

As online learning flourishes, it’s easier than ever to learn new things. If we search for virtually any technology, we’ll get remarkable amounts of results. In some sense, it’s good, since we have the freedom to choose. But how should we make that choice?