I am going to start with something that is timeworn and you have likely heard already – the times have changed.
And I am not saying in the context of a dark, dystopian movie. They have changed for the good.
Not very long ago, in 2019, if I had to find the recipe for Quesadilla and the ingredients I needed to bake it, I’d just Google it, list the ingredients down, and then buy the ingredients from Walmart. Now, I’d rather ask my Google Home Pod or ChatGPT and order it online. Or else, I’d just tell ChatGPT what I already have in stock and ask it to help me make something out of it.
Similarly, things have changed in software development, especially because of the new AI-powered tools that make it super easy for developers to focus on what matters and leave the humdrum stuff for tools.
From spotting bugs in your software to even writing the code, they can help developers.
Having said that, there are hundreds of tools out there on the internet. Some of them will just waste your time, the way they did mine.Developers who want to better understand how modern AI tools work in practice can explore learning resources and tutorials available on Coursiv. To save you from such hassle, let me share the best tools, their use cases, and more about them.
Are you ready to improve your code quality and efficiency? Yes? Let’s dive right in.
Contents
Pros of AI Tools Along with Their Use Cases
The most common use case of the AI-powered tools in software development is automated testing.
Let me explain why it is common.
Human brains have a limited capability to think about all the possible scenarios. They might be creative, but they cannot cover all the aspects of testing. That’s when the AI tools step in to take charge and offer unimaginable scenarios.
Little human intervention is needed, but it reduces the time taken for the software to be deployed.
Apart from that, before starting the project, there is a phase called planning, which is usually done by the head of the development team. And in that phase, many humdrum tasks need to be completed, like choosing the architectural design. Once you have fed AI tools about your requirements, it makes it easy for you to decide on the architecture.
On top of all that, it can reduce the need for manual documentation and coding. You can always rely on the AI-generated code and the documentation. But don’t turn a blind eye there, because it is still not perfect, and you will need human touch in the end.
There are more use cases like security optimization, UI/UX ideation, and stuff like that. In simple words, AI-powered tools are seriously important.
Best AI Tools Tailored for Software Developers
Now that you know about some of the use cases of AI tools for software developers, let’s take a look at some of the best ones.
GPT-Engineer
This is the best tool when it comes to boosting the efficiency of the developers.
Why am I saying that?
It’s because one of the most tedious tasks in the development phase of a project is generating the code, and GPT-Engineer helps with exactly that. Long gone are the days when developers would manually type thousands of lines of code just to get the foundation ready.
Not only can GPT-Engineer build the foundation of the code, but it can also help make it better. With refined and detailed prompts, it can make the code better to produce an improved output. In my usage, it helped me with programming languages as well. I wasn’t very familiar with Java, but GPT-Engineer was. It created the foundation, and I refined it after getting used to Java.
That said, keep in mind that these tools don’t run locally on your system because they rely on cloud technology.
To make sure they work seamlessly, I recommend having a reliable internet connection first. For example, I rely on Spectrum Internet, which hasn’t let me down in any of my projects. If you’re looking for a reliable internet plan based on your needs, you can reach out to their support, which you can also get in Spanish through Spectrum Servicio al Cliente en Español.
On to the next best tool that helps developers with efficiency.
Copilot
Copilot is the next best thing you will find in the space of AI-powered tools after GPT-Engineer. It works just like the previously mentioned tool, but requires a bit of manual work too. Let me explain.
GPT-Engineer will do everything from scratch based on the prompts and requirements you have given it. On the flip side, Copilot will be integrated with your IDEs, and it will be a guide while you walk on your feet.
It’s more like a teacher than a tool.
It doesn’t mean that it can’t be helpful. It is still very helpful for the software development teams that rely on manual coding rather than just prompting.
As a developer, you will learn new techniques on how you can improve the quality of the code, and stuff like that. This tool is perfect for anyone looking at the high-level coding while leaving the basic stuff for the Copilot.
Anyway, both of them are great tools that I think you should try out. However, while these tools can be helpful, it’s important that you know how to code so that you can find and fix any mistakes.
