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How AI Can Boost Developer Productivity

Let’s dive in and explore how AI can help developers or engineers be more productive, more accurate, and deliver better overall quality. Some people have gone as far as to say that in a few years, nobody’s going to need to write code anymore - all you’ll have to do is talk. While I wouldn’t go that far, I do believe there are many tools available today that can significantly enhance developer productivity.

Code Generation Tools

The first area we’re going to examine is the code generation family. If you’re unfamiliar with this, these are extensions, typically integrated into your IDE, that help complete the code for you. In some cases, you can ask for suggestions, and it will write code; in others, it will look at the keystrokes you’re performing and suggest auto-completion on how to write the best or most optimal code.

This can be extremely helpful because let’s face it, not many developers are going to remember every single class name for every single library they’re trying to use off the top of their heads. They know what they need to get done but quite often must stop, pause, look up which class they’re trying to use, and then implement it. The code generators can provide a lot of value in this area as they’re going to be looking at what you’re trying to do and already know the library that’s best used for that circumstance. In some cases, depending on the toolset, it can also complete code based on natural language you supply to it.

As a general statement, this classification of tools will make your developers more productive. I have seen gains of up to 60%. However, your workloads, your developers, their competencies are all going to vary the actual gains that these tools will give you. It will take a much deeper dive into your team to figure out exactly how much gain. So let’s take a look at some of the more popular tools in this area:

Which toolset is right for you will depend on several factors such as what languages you’re coding in, what IDEs you’re using, and what type of gains you’re looking to get.

Unit Testing Tools

The second area of tools we’re going to examine is unit testing. Unit testing is a key area for developers or engineers so that they can ensure the code they are creating has a high quality of success. By performing unit tests, it allows developers to test their code in very small portions or units of work. However, adding unit tests manually can be something that is very tedious and can take up a lot of time for the developers. It is one of the reasons why developers will often fight against the adoption of unit tests. So, since it is such a tedious process, why not take a look at how AI-based toolsets can assist here. Below are a few tools that I can recommend:

QA Automation Tools

The last area that we will examine is QA automation, a traditionally heavy manual area of testing inside of your organization or applications. So how can AI-assisted tools help in this area? There are a few ways that tools can help in this area. One is by examining your logs and seeing actual user actions that are happening within your application and automating them to be run through QA processes. Secondly, many of these tools will generate and give you test ideas. This is based on the type of application that you have and the code base that you’re working with. It will use this to give you suggestions on what type of test you should be implementing into your QA processes, then generate those tests. Another area that often gets overlooked is accessibility. Depending on your application, you may have a requirement to be in compliance with certain accessibility laws. AI-assisted tools can generate the tests needed to make sure that you have the proper coverage within your application to meet those accessibility laws. So with this in mind, let’s take a look at a few of the recommended solutions that you should be looking to implement into your QA process:

While I supplied a list of recommended solutions, I did not break them down into why I recommended them. One of the reasons I took this approach was to stay very vendor-neutral and only give recommendations based on products that I have worked with. It will be up to you to assess how these tools might work for you based on your programming languages, IDEs, types of applications you’re developing, and who your end users are. For example, the use cases between B2B versus B2C can be very different and have very different requirements.

As always, if you do have further questions, we are here to help.