5 Interesting AI Coding Tools Programmers Must Know About

AI is revolutionizing numerous sectors, enabling individuals and businesses to save time, reduce costs, and produce high-quality applications. AI-powered tools automate tasks, enhancing productivity and creativity. This technological evolution is profoundly transforming the software development landscape, too. In fact, it is reshaping problem-solving approaches, and boosting the efficiency and creativity of developers globally. Advanced AI tools designed to augment developers’ capabilities, streamline development processes, and drive innovation are at the core of this revolution. AI gives developers the leeway to focus on unique problems and innovations while it performs mundane and repetitive coding using these vital tools. 

Programming, the backbone of modern software development, involves creating innovative applications and systems that power the digital world. Despite its complexity and challenges, the advent of AI-assisted coding tools has revolutionized the programming landscape, significantly enhancing the coding experience for developers. What’s more, these AI code tools make programming easier for beginners as well.

What are AI Code Tools?

Essentially, AI code tools are designed to assist programmers in their coding tasks. These tools significantly boost developer productivity by automating parts of the programming process and optimizing workflows. AI code tools learn from massive global codebase datasets. 

By recognizing patterns and commonalities in code, they can suggest completions, autocomplete lines or blocks of code, detect and fix bugs, and recommend best coding practices. For example, while writing a function, an AI code tool might suggest a relevant name, predict subsequent lines of code, or identify potential errors.

How is it Possible?

These predictive capabilities are possible due to Machine Learning (ML) models trained on millions of lines of code. The benefits of using AI code tools are substantial. They save developers time by reducing repetitive coding tasks and minimizing debugging efforts. It allows developers to concentrate more on the logical and creative aspects of programming, leading to better code quality and quicker development cycles. The rise of AI code tools marks a significant advancement in software development, providing programmers with a smart assistant to enhance coding efficiency and effectiveness.

Why AI Coding Tools?

AI coding tools are invaluable, offering features such as autocompletion, natural language prompts, and direct code requests. These tools can suggest code completions as developers type. They can translate plain English descriptions into code snippets and also generate specific code or fix bugs based on direct requests. This functionality enables developers to write code faster and more accurately, optimizing their productivity.

The widespread usage and accessibility of generative AI solutions are a significant innovation for programmers. These AI tools are used for various applications, from creating artwork to writing code. Incredibly, their growth has outpaced every consumer technology trend of the past decade. Many developers have integrated AI coding tools into their workflows as the software development landscape evolves. These tools leverage advanced AI models to optimize coding tasks, allowing developers to interact with AI assistants and chatbots to enhance their coding experience.

Here are some of the interesting AI coding tools for programmers:

GitHub Copilot

GitHub Copilot is an AI coding assistant that supports multiple programming languages, such as C++, TypeScript, Python, JavaScript, and Go. Trained on extensive datasets from public repositories and other sources, it generates helpful code suggestions and content. Copilot aids developers by identifying lousy coding practices and offering improvement tips, enhancing coding efficiency and quality.

Tabnine

Tabnine includes code recommendation and completion capabilities that help programmers produce high-quality code faster. It is built on Large Language Models (LLMs) trained on massive volumes of open-source code. Tabnine checks code in real time, detecting problems and errors as they occur. As a result, it saves developers time that would otherwise be spent editing by assuring error-free code while they write.

OpenAI Codex

OpenAI Codex is a general-purpose model that allows people to generate code using natural language prompts. It carries out responsibilities such as code review and technical content development. You may also use OpenAI Codex to add comments to your code, convert existing comments to code, and discover helpful APIs and libraries. OpenAI Codex specializes in Python but provides minimal support for other programming languages like Swift, Perl, and PHP.

CodeT5

CodeT5 is a platform that allows developers to generate AI-generated code for their specific initiatives. It is a free online AI code generator readily accessible to developers worldwide. The tool employs Natural Language Processing (NLP) and ML techniques to analyze project requirements and generate the most optimal code possible using pretrained models. It can complete, summarize, and convert incomplete code between multiple programming languages.

AI2SQL

The AI2SQL code generator was created to convert naturally occurring natural language queries into SQL statements. By seamlessly translating natural language into SQL, AI2SQL reduces the need for a thorough mastery of SQL syntax, making database interactions more accessible to a broader audience. Python programmers, data scientists, and business executives who need to investigate databases for insights can all benefit from the capabilities of this novel AI code generator.

The Future is AI—in Coding

AI coding tools assist programmers at various stages of the development lifecycle. They can generate boilerplate code, offer tips and suggestions, perform debugging, and conduct unit tests to ensure the delivery of functional and reliable products. AI coding tools also automate monotonous tasks such as autocompletion and error detection. This enables developers to focus on creative problem-solving and logic-building. 

However, the proliferation of AI coding tools presents a challenge in finding the right one. As available tools grow, developers must navigate this expanding landscape to identify the most effective AI coding solutions for their needs.

NOTE: The views expressed in this article are those of the author and not of Emeritus.