The Rise of AI Coding: The 5 AI Assistant Tools to Use this 2023

July 6, 2023

The Rise of AI Coding The 5 AI Assistant Tools to Use this 2023

Research done by Marielle

Artificial Intelligence (AI) is dominating the tech world this 2023 as companies across all industries are turning to intelligent virtual assistants to streamline their processes and customer service. And it is also here to assist developers in how they write code.

These are called AI code tools, and they are becoming increasingly popular in helping the software development process. How do these code tools work?

AI-assisted coding unleashes the power of machine learning models to generate code effortlessly. With cutting-edge AI tools, developers can describe what they want their code to accomplish in natural language. The AI then translates that description into code and offers suggestions for improvement.

Let’s dive deeper into what these AI code tools are and how they can help developers be more productive!

10 Benefits of Using AI Code Completion

Fast equals accurate when it comes to coding. AI code completion tools can speed up the development process while also providing accuracy with fewer errors. If you’re a developer, you know the importance of quickly and efficiently writing code.

Here are the 10 main benefits of using AI-assisted coding:

1. Reduction in Human Error

By automating repetitive tasks and conducting code analysis, AI coding tools can greatly minimize human error. They are capable of doing code reviews and identifying syntax errors, logical inconsistencies, and typos, thereby saving precious time and effort during the debugging process.

2. Zero Risks

AI coding tools provide real-time feedback and identify potential vulnerabilities, security loopholes, and performance bottlenecks in software development. By addressing these issues early on, developers can ensure a more robust and secure codebase, minimizing risks.

3. 24/7 Availability

Continuous 24/7 availability of AI tools enable developers to access code tools at any time. This ensures productivity outside of regular working hours and minimizes downtime, providing continuous support and assistance.

4. Digital Assistance

Digital assistance in coding is made possible through an AI-powered code assistant. These AI-powered tools streamline the coding process by offering code completion suggestions, generating boilerplate code, and providing documentation references.

5. New Inventions

AI-powered analysis of code repositories inspires developers to create innovative solutions by leveraging patterns and existing implementations. This facilitates new inventions and helps tackle complex coding tasks with fresh approaches.

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6. Unbiased Decisions

AI tools enable unbiased decision-making by providing impartial feedback and suggestions, ensuring consistent code quality, and reducing human bias. Natural language processing (NLP) techniques help the code assistant to understand complex commands without any bias toward certain frameworks, languages, or methodologies.

7. Perform Repetitive Jobs

Automating repetitive programming tasks, AI coding assistants free up developers’ time to focus on more challenging and creative aspects of software development. They excel at automating code generation, documentation generation, and code formatting, improving efficiency and productivity.

8. AI Manages Risky Situations

Artificial intelligence helps manage risky situations by providing predictive analysis and risk assessment. These coding tools are equipped to handle risky situations, identify potential security vulnerabilities, and suggest mitigation strategies.

9. Faster Decision Making

AI code generators enable faster decision-making for developers by processing large amounts of both source code and data quickly. With prompt insights and actionable recommendations, developers can iterate and make informed choices without unnecessary delays during the development process.

10. Pattern Identification

AI coding tools excel at pattern identification, analysis, and trend detection. By analyzing extensive codebases and historical data, creating high-quality code, optimizing performance, and adopting efficient coding techniques is easy.

5 AI-Assisted Coding Tools

With all the benefits it offers, AI-assisted coding is gaining popularity among developers.

Probably the most famous are Github Copilot (which is based on OpenAI Codex) and ChatGPT. In the realm of AI code completion, even smaller companies like DeepCode and Tabnine are stepping up with production-ready tools.

Below are the top artificial intelligence assistant coding tools:

OpenAI Codex

One of the most prominent AI code assistants available today is OpenAI Codex. It’s based on GPT-3 and is most capable in Python, but it is also proficient in over a dozen programming languages, including JavaScript, Go, Perl, PHP, Ruby, Swift and TypeScript, and even Shell.

The features of this tool:

  • cloud-based
  • It powers GitHub Copilot
  • Codex automatically generates code based on given prompts
  • develops only one possible solution for every given command
  • seems more appropriate for non-programmers who want to generate simple SQL queries and similar commands or learn simple coding skills
  • ability to manipulate other software using natural language commands:
    • For example, a demo showed that Codex could correctly generate code that instructs Microsoft Word to perform functions like deleting all initial spaces and the last line in a document
    • Other tests by OpenAI confirmed that Codex can also control Spotify and Google Calendar. However, this “control” feature may require some improvements before it’s further publicized and commercialized.
  • Codex can be applied in game development, data science, and many other industries. It may also help developers who use different programming languages better understand each other and work faster.


Sample output of OpenAI Codex

GitHub Copilot

Copilot Uses publicly available code from GitHub repositories so that users can access large datasets and quickly develop accurate code. The tool detects errors in code and recommends changes to it. In addition, GitHub Copilot is great for optimizing existing code to make it run faster.

There are three main ways to get assistance from Copilot:

  • Inline suggestions: Harness Copilot’s help automatically through suggestions it provides directly inline as you work in your code.
  • Chat view: Ask Copilot for help with any task or question in the GitHub Copilot Chat view.
  • Inline chat: Talk with Copilot while writing code inline in your files.

The features of this tool:

  • Offers autocomplete styles suggestions as you code
  • Assisting with HTML & CSS
  • Facilitating bootstrap
  • Regex simplified
  • Unit testing made easy
  • Plans and Pricing
    • If you want to use GitHub Copilot, you either need a subscription for GitHub Copilot in your personal account, or you need to be assigned a seat by an organization with a subscription for GitHub Copilot for Business.
    • you can set up a one-time 30-day trial to evaluate GitHub Copilot
  • You know everything is set up correctly and is working when it starts giving you predictions as you start coding, such as you will see that it is shaded in gray.


Sample output of GitHub Copilot


Created by OpenAI, the AI chatbot GPT has become hugely popular. It is a sibling model to InstructGPT, which is trained to follow instructions in a prompt and provide a detailed response.

As an AI coding assistant, ChatGPT can be used in a variety of applications. It can assist you with:

  • code completion
  • code formatting
  • generate documentation
  • translate languages
  • grammar correction
  • error detection
  • debugging
  • refactoring
  • automate testing

ChatGPT is not just any ordinary artificial intelligence tool. It possesses the incredible power of remembrance, effortlessly recalling previous user inputs during a conversation. This nifty feature allows users to effortlessly provide follow-up corrections and clarifications, resulting in an interaction that is both seamless and remarkably accurate.

Additionally, the model has been trained to identify and decline inappropriate requests, promoting a safe and respectful environment for all users. With its advanced capabilities, ChatGPT aims to deliver an enhanced conversational experience that is both informative and engaging.


Sample output of ChatGPT


Tabnine is an AI code completion tool that utilizes deep learning algorithms to provide the user with intelligent code completion capabilities. Tabnine supports several programming languages, such as Java, Python, C++, and more. This tool is open-source and is used by leading tech companies like Facebook and Google.

The features of this tool:

  • Supports all major IDEs(VS Code, WebStorm, PhpStorm, Vim, Sublime and many more)
  • Supports the most popular languages, libraries & frameworks(React, Angular, NodeJS, C, PHP, Swift, Java, HTML, CSS, Javascript and many more)
  • Easy setup (Visual Studio Code, for example)
    • They have direct or manual installation
    • You know everything is set up correctly and working when you start coding. You’ll see Tabnine’s code suggestions inline in gray, you will see a Tabnine AI Assistant in your VS Code left menu, and in the status bar, there will be a Tabnine icon for you to manage your settings, get help, or more.
    • Hit Tab to accept a code suggestion
    • To view code suggestions for a new line, hit Enter.
    • They recommend working on a file that already contains code to provide Tabnine’s AI model with sufficient context.
  • As you type, Tabnine Pro serves whole-line, full-function, and even natural language to code completions. You can accept your whole line completion or keep typing to get more real-time alternatives that keep adapting to your code context.
    • Private & Secured
    • Tabnine Pro AI completions can be run on a developer’s laptop, on a server inside your firewall, or in the cloud.

Tabnine has three plans you can choose from:

  • Starter/Free (Basic code completion)
  • Pro (Advance code completion)
  • Enterprise (Tailored AI models)


Sample output of Tabnine


A cloud-based AI code analysis tool that automatically scans the entire codebase of a project and identifies potential bugs and vulnerabilities. It offers support for various programming languages, such as Java, Python, and JavaScript. DeepCode is well-regarded for its accurate bug detection.

The features of this tool:

  • Snyk code is powered by the DeepCode engine
  • No setup
  • Extremely fast
  • Lots of integrations to choose from
  • Easily import and scan your project code based on your selected integration
  • It makes developer efforts efficient and actionable

When Snyk Code spots an issue, it doesn’t just point it out. It goes the extra mile and gives you real-world fix examples. These examples are based on similar patterns and data flows found in other projects. They’re like little nuggets of inspiration to help you solve the problem and save you from wasting time on expensive research.

DeepCode has three plans you can choose from:

  • Free Forever
  • Team
  • Enterprise


Sample output of DeepCode

Drawbacks of Writing Code with AI

AI code completion tools are incredibly useful, but they come with their own set of drawbacks. While these tools can enable developers to work more efficiently and show code snippets accurately, there is the potential for errors if not used correctly.

Here are the limitations of AI-powered code completion tools:

1. High costs

AI-powered coding tools usually come with a high price tag. For many organizations, the cost associated with these tools can be too much of an expense. As such, some developers may not be able to use the full features or capabilities of these tools due to budget constraints.

2. No creativity

Though these tools can generate high-quality code, most of the time, it writes code in a generic way. Developers may not be able to express their creative flair and originality when coding with AI-powered tools. This could result in limited creativity and innovation, which can lead to substandard code.

3. Unemployment

The rise of AI code generator tools has the potential to lead to job losses. As more and more developers become reliant on these tools, fewer jobs will be available for human coders. This could have a negative effect on the industry and might cause an increase in unemployment rates.

4. Make developers lazy

The presence of AI code generators may make developers lazy and unwilling to think through their own development processes. This could lead to complacency, which can have serious consequences on the quality of the code. Developers should take responsibility for their work instead of relying too much on these tools.

5. No ethics

AI coding assistants are not equipped with ethical considerations, which could lead to code errors or to unwanted results. Developers should be aware of the potential consequences of using these tools and take steps to ensure that they are used responsibly. Developers need to have an understanding of ethics when coding with AI-powered tools.

6. Emotionless

Writing code with AI can be cold and emotionless, lacking the ingenuity and passion of human coders. This could lead to developers feeling disconnected from their work and having a lack of motivation to do their jobs. It’s important for developers to engage in meaningful conversations about code and create an environment where they can explore their creative sides and coding practices.

7. No improvement

Knowledge and experience with using AI code generation tools look good on paper, but they don’t necessarily make developers better coders. As such, the use of these tools should not be a replacement for developing and honing coding skills. Developers should strive to continually develop their coding abilities rather than relying on AI tools to do all the work.


We can conclude that AI code suggestion proposals have already had a tremendous influence in helping programmers and code editors reduce repetitive tasks. AI-assisted code suggestions will enable developers to speed up coding, debugging, refactoring, documentation, and many more tasks, greatly enhancing the software development lifecycle (SDLC).

Whatever the case, it’s important that these AI-assisted coding tools should be seen as a tool to help and support the software development process rather than a substitute for human creativity and expertise.

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About the author: Andrea Jacinto - Content Writer

A content writer with a strong SEO background, Andrea has been working with digital marketers from different fields to create optimized articles which are informative, digestible, and fun to read. Now, she's writing for StarTechUP to deliver the latest developments in tech to readers around the world. View on Linkedin