At the moment, software developers interested in AI and machine learning talk about building apps with AI and the instruments for Artificial Intelligence-based development. We can name solutions like PyTorch, TensorFlow, etc.

However, machine learning tech is affecting the programming world in yet another interesting way. We mean the recent software that uses machine learning algorithms to enhance the developers’ workflow. In this post, we will mention 5 of them. 2 of them are still in testing mode while the remaining three are already available. If you need to figure out how to use AI quicker and more effectively in software development, you can check out these solutions.

1. DeepCode

DeepCode is one of the successful machine learning tools for software development. Its main purpose is to test code and highlight parts that could be vulnerable to security breaches. Much like some of other tools we mention further, DeepCode checks code from public storage places to identify similarities. Additionally, this tool makes use of the patterns to find vulnerable areas. 

DeepCode analyzes user input handling before the critical security level is attained. Consequently, when any data moves from one point to another without any security verification or clearance, the tool identifies it as tainted and informs you about it. Amongst the issues that this tool is provided to highlight are cross-website scripting, SQL injection threats, remote code execution, and path-traversal strikes.

You can find analyses done with DeepCode on popular repositories such as Bitbucket and even GitHub. The reports are free of charge and can be used for open source projects or personal projects of thirty or fewer programmers. You can additionally use DeepCode for investigating your on-premises code hosting at a cost.

2. Kite

Kite acts as a code completer. With the help of machine learning, it easily identifies the code you are inputting in real-time and builds it as you type. Regularly listed as one of the most useful tools for developers, it works well with many modern code editing solutions. 

Kite uses an effective model taken from GitHub. GitHub code, which is convenient to all, is used to produce an abstract that acts as the backbone of the Kite model. As before-mentioned, the tool can propose and even complete code automatically, based on both connection and purpose instead of just the text itself. 

When it was first started, Kite could only be reached on Mac and Windows. Now, it can be used on Linux also. The downside of this tool is that it only works with Python at the moment. At this point, it’s being developed to run with Go, too. 

Two years ago, Kite was accused by open-source programmers of mistreating users’ data as well as transforming a popular Atom plugin that autocompletes one’s code. However, the owners have since solved both issues. Lately, the Kite organization published that the tool can now operate all functions locally within the user’s PC and not in the cloud, as it was done before. 

3. PROSE

This tool, produced by Microsoft, helps to generate code with the use of examples. PROSE stands for “program synthesis using examples” and can be leveraged to generate other programming tools, as opposed to implementing it directly as a predictive solution. Amongst the ways that a developer can use PROSE is file manipulation through example, a text conversion through example, and extracting data from a text file.

4. Codota

This tool is pretty much like a Kite in that it uses machine learning to create computerized code completions. Furthermore, it uses a type formed from the syntax tree that is acquired from publicly accessible code. Though, it seems to have some exceptions.

Codota is created for Kotlin and Java languages. It is a cloud-based solution that produces smart auto predictions. It is worth noting that Codota owners affirmed that user info is not transmitted to their servers. Just the limited encrypted data from the edited text is transmitted as it is required to predict code in line with the field and context. 

You can use Codota on Linux, Windows, and Mac devices. But the editor mode only runs for Android Studio, Eclipse, and IntelliJ, which is understandable when you look at the languages the tool supports. In addition to this, the Codota creators noticed that the versions for other programming languages are in progress, with JavaScript support expected to be the first one. 

There is a free version of this tool, which generates auto-suggestions solely from publicly-accessible code. Though, the paid one can also use private code. You can request the quote on the tool’s official page. 

5. Pix2code

Pix2code is currently in the experimentation phase, though it is an innovative tool that can turn a graphic user interface screenshot into computer code. By employing deep learning methods, this software can analyze GUI in three various formats: Android, iOS, and HTML/CSS. But considering this tool is still in the testing stage, you can only use it for learning or as a basis for additional software development. 

Advantages of Employing Machine Learning Tools for Software Development

As you see, these innovative tools can help with code fulfillment, security measures, and even code generation. Machine learning provides great opportunities, as well as quick and productive software creation, so it is worth checking out the mentioned tools. That being said — no single tool can run without a team of experienced software developers. These tools are essential if it comes to saving time on development, but the initial processes of planning and the final processes of testing, QA, and deployment will require experienced developers.

Guest article written by: Sandra Parker works as a Business Developer in QArea and Testfort. She helps enterprises to accelerate their businesses through custom software development and testing.