Experts List the Best AI Coding Tools to Use

With Artificial Intelligence (AI) gaining greater knowledge in the world of coding and programming, some experts believe that it is only a matter of time before AI fully replaces human coders. Although there were many issues with early AI coding and some problems persist, the overall quality of AI coding has improved drastically since its early introduction.

These improvements have left many experts excited at the potential prospects of the industry as some tools have the potential to disrupt the industry. Business owners need to look at the available tools and examine which are useful. With so many AI projects happening, there are also issues of some coding tools being derivative and ineffective.

To help with this problem, some experts have listed the best AI tools your business can use for its programming needs. These have been tested and experts have determined the AI models with the most use cases or potential for improvement.

How is AI Coding Tested?

AI code automates programming by generating code based on open-source data. This allows the model to place code that it believes fits your requirements and what you want it to do. The models will generate and then review this code to ensure it is accurate and error-free. Machine learning can detect errors, enhance performance, and suggest improvements.

Its strength lies in its quickness and ability to handle large volumes of code, spotting patterns, and anomalies. Unlike humans, AI doesn’t tire or show bias, ensuring consistent and accurate assessments. The problem is AI coding doesn’t understand nuances and context so while it might understand code on a literal level, it can be confused, leading to many cases of AI generating bad code. It is important to realize this when harnessing AI to prevent making mistakes in your business.

One way that AI code is tested is with static code analysis. This involves inspecting code without running it to find potential issues like syntax errors, standard violations, and security risks. It’s great for finding problems and offering detailed reports, while AI can suggest improvements.

The next step could be dynamic code analysis is to test the code and see what it can do. Testers can then observe performance bugs and other issues that might emerge. This allows the testers to find a complete understanding of the performance and what can be improved.

Having the AI access large datasets and train them with that information. This setup will allow the AI setup to learn and. improve over time as it learns and is given feedback by testers. 

Most Reliable AI Tools for Programming

After careful testing, these were considered the best AI coding tools. These models were determined based on their ability to generate code along with the quality and accuracy of that code.

OpenAI Codex

OpenAI continues to remain at the forefront of AI innovation with their tools being used and powering other AI projects, ensuring industry innovation. Their OpenAI Codex remains one of the most advanced coding tools in the industry. The AI model can write 12 coding languages including BASH, JavaScript, Go, Perl, PHP, Ruby, Swift, and TypeScript. 

This has been used to power other models such as GitHub Codespaces, VS Code, JetBrains, and Neovim. The algorithm remains useful because it has been trained on countless lines of code that is public sources such as GitHub repositories. 

At the moment, this system is still in its beta stages, but OpenAI has recently made it accessible for developers to test.

Tabnine

Another promising AI model is Tabnine which has become useful in enhancing integrated development environment (IDE) auto-completion. While not a complete code generator, this proved to be a useful tool. Tabnine was developed by Jacob Jackson at the University of Waterloo. the model has proven proficient in over 20 languages and 15 editors, including VS Code, IntelliJ, Android Studio, and Vim. 

CodeT5

CodeT5, a programming language model, was created by Salesforce researchers. It uses Google’s T5 framework and has access to 8.35 million instances of code and comments from GitHub projects and CodeSearchNet dataset, containing two C and C# datasets from BigQuery, along with Ruby, JavaScript, Go, Python, PHP, and C and C#.

Polycoder

Developed by Carnegie Mellon University scientists, Polycoder has given OpenAI’s Codex some competition. It has taken a basis on GPT-2 and has been trained on 249 GB codebase. They have been trained in 12 languages, making a highly versatile tool in your arsenal. The creators claimed that Polycoder can write code more accurately than tools like Codex.

Cogram

This is created by Berlin’s Y-Combinator incubator and promises to simplify coding tasks for data scientists and Python programmers using Jupyter Notebooks and SQL queries. Data scientists can write English-language queries which Cogram converts into advanced SQL queries, incorporating joins and grouping. It supports MySQL, SQLite, PostgreSQL, and Amazon Redshift as its coding languages.

Integration with these coding languages allows developers to generate code automatically and allows the model to offer contextual code suggestions to improve the outcome of your project. 

GitHub Copilot

This Microsoft-built platform is powered by GPT-3 and OpenAI Codex and has been one of the most effective coding tools built. This is thanks to the countless resources Microsoft has built onto its platform. The platform can generate code and help you review existing code, making it popular with coders working on a time crunch or are inexperienced. This platform can speed up the coding process while ensuring high quality by training your coders.

DeepCode

One of the newer AI-powered code review tools is DeepCode, a model that enhances your code by providing suggestions. Among the features are completing code, realigning it, and refactoring it. The model is free to use in open-source projects and has a premium service if you want to invest more heavily in coding projects.

Jedi

Another open-source option for AI code completion is Jedi. This model functions as a plugin for Python static analysis tools in IDEs and editors. The AI primarily works for code auto-completion but provides other functionalities like goto, search, find, and refactoring. Its simple API allows integration with various editors and projects in your company.

Wing Pro

This is a smart editor designed for efficient Python coding. It analyzes both static and dynamic code to offer relevant recommendations. This allows it to be quickly added into most business setups with little difficulty. Its features include error-checking, auto-completion, built-in Python shells, and a Source Assistant for inline documentation and call suggestions. It also helps with browsing invocations and managing parameters effectively.

All of these AI tools have shown to have potential in your business as they can help coders improve their abilities while adding automation to the programming process. Here at geniusOS, we are reviewing all the uses of these coding models and integrating them into our process. If you want to learn about the tools we use and what services we have to offer, you can reach out to us here.

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