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Best Machine Learning Tools to Modernize your Software Development

In the current scenario, when developers get immersed in the discussion about making use of machine learning, they are limited to creating AI-powered applications and the tools used to create them. They include TensorFlow, PyTorch, Scikit-learn, etc.

However, there is one other way machine learning is also impacting software development. ML has penetrated the software development ethos. Current development tools use machine learning techniques to make programming easier and more productive. Most of these tools make use of ML in order to provide better output, speedy development, and long-standing future-proof results.

Let us look at some of the most interesting Machine Learning-based tools that will enhance your software development process


Codota uses a machine learning model, trained on Java and Kotlin code, which suggests autocompletion for these languages as you type. Codota utilizes the code’s syntax tree, not merely its text, as the data for building its models.

It has a cloud-based service that generates and serve predictions. It should be noted that Codota does not send user code to the Codota server, it only lifts minimal contextual information from the currently edited file that allows the tool in making predictions based on the current local scope.

Codota is available for Windows, macOS, and Linux, but editor support is limited to IntelliJ, Android Studio, and Eclipse.

Microsoft PROSE 

PROSE stands for “PROgram Synthesis using Examples.” Microsoft built this project as an SDK for generating code from sample input and output. This makes PROSE a toolkit in order to build predictive coding tools, rather than a predictive coding tool itself.

The potential PROSE applications include transforming text by example, extracting data from text files like log analytics, and predictive file manipulation that includes splitting text into columns by example.


Kite is another fantastic code completion tool. It is available for most major code editors, that uses machine learning techniques to fill in your code as you’re typing it.

Their machine learning model has been created by capturing publicly available code on GitHub, deriving an abstract syntax tree from it, and using that as a basis for the model. Currently, Kite is only available for Python developers. For future development, Go support is in the works. Kite was originally available only for Windows and macOS users, now it supports Linux too.

Kite does not send user code back to its cloud servers, but performs all processing locally, and explicitly acknowledging that the autocomplete-python package is a Kite-sponsored package.


This tool magnificently performs automated, AI-guided reviews of code that detects potential security vulnerabilities. DeepCode has the capability to analyze code available in public repositories to look for common patterns. DeepCode makes use of these patterns to identify security holes. 

The tool focuses on “taint analysis,” which dictates how user input is handled before it reaches any security-critical point. The data that goes directly from user input to, say, a SQL query, without verifying that it’s safe to pass along, is considered “tainted” and raises an alert.

Most of the critical bugs that DeepCode claims to address include common security issues found in web applications like, cross-site scripting, SQL injection attacks, remote code execution, and path-traversal attacks.


The future of development will also be powered by Artificial Intelligence and Machine Learning. Be it Java-based development or Python development, Microsoft services C# development or ASP.NET web application development, the future looks productive and the time taken to develop modern applications will reduce drastically. 

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