Comparing GitHub Copilot and ChatGPT

GitHub Copilot vs ChatGPT: Comparing AI Coding Assistants

AUTHOR

Ted

CATEGORY

Comparisons

DATE

April 25, 2023

Writing code can be challenging and time-consuming, especially when dealing with complex problems, unfamiliar languages, or repetitive tasks. Luckily, there are tools that can assist you in writing code faster and with less e몭ort. Two such tools are GitHub Copilot and ChatGPT, both powered by arti몭cial intelligence and natural language processing to suggest code based on your input

suggest code based on your input.

But how do they work? What are the di몭erences between them? And which one is better for coding? This article compares GitHub Copilot vs ChatGPT, looking at their features, use cases, and pros and cons. By the end, you’ll understand these tools better and how they can help with your coding projects.

So, let’s dive in!

Table of Contents

1. What is GitHub Copilot?

2 What is ChatGPT?

3. Comparison of GitHub Copilot vs ChatGPT

3 1 Accuracy and Relevance of Suggestions

3.2. Code Completion Speed

3 3 Ease of Use

3.4. Programming Languages Supported

3 5 Integration with Code Editors

3.6. Customization Options

3 7 Training Data Sources and Bias Concerns

3.8. Privacy Policy and Data Usage

4. GitHub Copilot: Pros and Cons

4 1 Pros

4.2. Cons

5 ChatGPT: Pros and Cons

5.1. Pros

What is GitHub Copilot?

GitHub Copilot is an AI-powered tool that can help you write code quickly and with ease. It is based on OpenAI Codex, which is a pre-trained language model. It can understand the context of your code and comments to provide suggestions for individual lines and entire functions

functions.

To use GitHub Copilot, you can install it as a Visual Studio Code extension. You can describe the logic you want, and the tool will provide suggestions for implementing the solution Additionally, you can use natural language prompts to create code for speci몭c tasks like sorting a list or parsing a JSON 몭le.

GitHub Copilot can suggest code in various programming languages, such as Python, Ruby, Go, C#, and more. It can also provide multi-line function suggestions, accelerate test generation, and block suggestions that match public code.

There are two subscription plans available for GitHub Copilot: Copilot for Individuals and Copilot for Business:

Copilot for Individuals costs $10 per month and includes features for coding

Copilot for Business costs $19 per user per month and provides everything in Copilot for Individuals, plus premium features like simple license management, organization-wide policy management, privacy, and corporate proxy support.

What is ChatGPT?

ChatGPT is an AI chatbot designed for conversational interaction, which can respond to your queries, discuss a wide range of topics, and even generate creative written content. The system is powered by OpenAI’s generative pre-trained language models, GPT-3 and GPT-4.

As an online service, ChatGPT is easily accessible via web browsers. Simply enter a message or prompt, and ChatGPT’s natural language understanding and generation abilities will produce a text response.

ChatGPT can converse with users in multiple languages, including but not limited to English, Spanish, French, German, Portuguese, Chinese, Italian, and Japanese It also o몭ers various natural language processing functionalities like translation, summarization, and paraphrasing.

Currently, ChatGPT is available to all users as a free service with the GPT-3 x model

Additionally, ChatGPT o몭ers a premium subscription service ($20 per month) that includes access to more advanced features and bene몭ts enabled by the GPT-4 model.

Comparison of GitHub Copilot vs ChatGPT

GitHub Copilot and ChatGPT are both AI-powered tools that can help you write code faster and with less e몭ort. However, they have di몭erent features, strengths, and weaknesses that make them appropriate for di몭erent contexts and scenarios.

The following table summarizes the di몭erences between GitHub Copilot and ChatGPT:

Aspect GitHub Copilot ChatGPT

Accuracy and Relevance of Suggestions

Code Completion Speed

Ease of Use

Programmin g Languages

Supported

Integration with Code Editors

Customizati on Options

Training

Data Sources and Bias

Concerns

Privacy Policy and Data Usage

Suggests code in real-time using OpenAI Codex, provides relevant suggestions across dozens of languages

Speeds up test generation and reduces boilerplate code, helps developers code faster

Simple and intuitive user interface that integrates with your IDE

Supports dozens of languages, including Python, JavaScript, Ruby, Go, Java, C#, and more

Integrates well with various code editors, such as VS Code, Neovim, and JetBrains IDEs

Allows users to customize some aspects of its behavior

Trained on massive dataset of human language text, raises concerns about potential bias or misinformation

Does not store user data unless explicitly authorized, warns against sharing sensitive information

Generates code through conversational prompts, sometimes writes incorrect or nonsensical answers

Separate tool, requires copypasting of code or prompts

Requires typing a message or prompt and reading response carefully

Supports multiple languages, such as Python, JavaScript, Ruby, Go, Java, C#, and more

Does not integrate with any code editors, but has a non-o몭cial VS Code extension

Does not o몭er any customization options

Requires typing message or prompt and reading the response carefully

Collects user data to train and 몭ne-tune service, no details on usage or protection of data

In the subsequent sections, we will examine each aspect in more detail.

Accuracy and Relevance of Suggestions

GitHub Copilot suggests code and functions in real-time, directly from your editor, using OpenAI Codex It utilizes comments and code to provide relevant suggestions in multiple programming languages.

ChatGPT interacts with you in a conversational way, using natural language prompts to generate code It can also answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. However, ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers, and is sensitive to tweaks to the input phrasing or attempting the same prompt multiple times.

Code Completion Speed

GitHub Copilot claims to speed up test generation and reduce boilerplate code Research has found that GitHub Copilot helps developers code faster, stay in the 몭ow longer, and feel more ful몭lled with their work.

ChatGPT is a separate tool, so you have to copy and paste your code or prompts into a chatbox and wait for a response

Ease of Use

GitHub Copilot has a user-friendly and straightforward interface that seamlessly integrates with your IDE, making it easier to use. You can simply write a comment or a prompt and get instant suggestions from Copilot

With ChatGPT, you have to type a message or a prompt to ChatGPT and read its response carefully to check if it has answered your question or concern.

Programming Languages Supported

GitHub Copilot supports dozens of languages, including Python, JavaScript, TypeScript, Ruby, Go, Java, C#, PHP, Swift, Kotlin, and more

ChatGPT also supports multiple languages, such as Python, JavaScript, Ruby, Go, Java, C#, SQL, and more. However, ChatGPT may not be able to handle some languages or frameworks that are not common or well-documented

Integration with Code Editors

GitHub Copilot integrates well with various code editors, such as VS Code, Neovim, and JetBrains IDEs. It can also work with other tools and platforms that support the Language Server Protocol (LSP).

ChatGPT does not integrate with any code editors, as it works as an online service that you can access through a web browser. However, there is a non-o몭cial VS Code extension for ChatGPT, but it requires your API key or token from OpenAI.

Customization Options

GitHub Copilot allows users to customize some aspects of its behavior, such as blocking suggestions matching public code or enabling experimental features.

ChatGPT does not o몭er any customization options for coding at the moment, which may limit its 몭exibility and adaptability for di몭erent use cases and preferences

Training Data Sources and Bias Concerns

GitHub Copilot is trained on a massive dataset of source code and natural language text from various sources, such as GitHub repositories, Stack Over몭ow posts, documentation pages, blog articles, books, etc. This makes it more diverse and comprehensive in its knowledge and skills However, it also raises some concerns about potential bias or plagiarism in its suggestions.

ChatGPT is trained on an enormous dataset of human language text from various sources, such as books, articles, websites, etc This makes it more general-purpose and versatile in its capabilities. However, it also raises some concerns about potential bias or misinformation in its responses.

Privacy Policy and Data Usage

GitHub Copilot states that it does not store any user data unless explicitly authorized by the user It also states that it does not use any user data to improve its model or service However, it warns that users should not share any sensitive or personal information with GitHub Copilot as it may inadvertently leak it in its suggestions.

OpenAI states that ChatGPT collects data from users to train and 몭ne-tune the service further However, OpenAI does not provide any details on how they use or protect this data GitHub Copilot: Pros and Cons

GitHub Copilot: Pros and Cons

GitHub Copilot is a powerful tool for faster and easier coding, but it has some drawbacks to consider Here are the pros and cons of using it:

Pros

Context-aware suggestions save time and e몭ort.

Multi-line function suggestions can handle complex tasks and logic.

Speeds up test generation and enhances code reliability. Supports various programming languages and cross-language suggestions.

Integrates well with di몭erent code editors and platforms

O몭ers customization options and allows feedback to improve suggestions.

Has a transparent and user-friendly privacy and data usage policy.

Cons

Suggestions may be irrelevant or incorrect.

Suggestions may infringe on intellectual property rights.

Suggestions may be biased or unethical in certain contexts

May not work well with some languages or frameworks

Requires a paid subscription, which may not be a몭ordable for everyone.

ChatGPT: Pros and Cons

ChatGPT is a versatile and general-purpose tool that can help you generate code with explanations and perform other natural language tasks However, it also has some limitations and drawbacks that you should be aware of. Here are some of the pros and cons of using ChatGPT for coding:

Pros

Generates code with explanations, ideal for beginners and learners

Performs various natural language tasks, such as translation, summarization, and paraphrasing, useful for di몭erent purposes and contexts

Chats across multiple languages and translates between them, accessible for users

Chats across multiple languages and translates between them, accessible for users from di몭erent regions

Accessible online service through web browser, portable and 몭exible for users on di몭erent devices and platforms

O몭ers some customization options and allows feedback to improve the responses

Available as a free service for anyone who wants to try it out

Cons

May generate incorrect or nonsensical code that does not meet your expectations or requirements

May generate code that infringes on intellectual property rights or is plagiarized

May generate code that is biased or unethical in certain contexts or scenarios

May not work well with some languages or frameworks that are not well-represented in its training data

Does not integrate with any code editors, requiring copy-pasting of code or prompts into a chatbox and waiting for a response

Less clear and user-friendly privacy policy and data usage policy that does not allow you to control your data

Use Cases and Scenarios for GitHub Copilot

GitHub Copilot o몭ers a range of use cases for code writing, here are the best ones:

Developing new applications or features: GitHub Copilot can help you quickly write code for everyday tasks like creating a user interface or connecting to a database. You can also generate code for speci몭c tasks by providing a prompt or comment

Refactoring or debugging existing code: GitHub Copilot can suggest improvements or corrections to existing code, helping you refactor or debug more e몭ciently. It can also generate code that follows best practices or 몭xes errors.

Writing tests or documentation: GitHub Copilot can assist in writing tests and documentation by suggesting comprehensive and relevant content. It can generate tests for various scenarios and edge cases, as well as documentation that explains functionality, parameters, return values, and exceptions.

Use cases and scenarios for ChatGPT

Use cases and scenarios for ChatGPT

ChatGPT can be used in various use cases and scenarios that involve generating code. Below are the top use cases:

Learning or improving coding skills: With ChatGPT, you can generate code with explanations and corrections to learn or improve coding skills. ChatGPT supports di몭erent programming languages such as Python, Java, and JavaScript, and various topics such as data structures, algorithms, and web development. ChatGPT can make corrections based on your feedback.

Summarizing or simplifying complex code: ChatGPT can help you summarize or simplify complex code It generates concise and clear text that describes the code’s functionality and logic. ChatGPT can also generate a starting template for a speci몭c coding task based on a brief description.

Performing other natural language tasks related to code: ChatGPT can assist you in performing other natural language tasks related to code It can summarize texts related to code, such as documentation pages, blog articles, books, etc. Additionally, ChatGPT can paraphrase sentences related to code, such as comments, prompts, instructions, etc.

Comparison with Other Code Suggestions and Language Models

GitHub Copilot and ChatGPT are just two examples of AI-powered code generation tools. There are several other similar or di몭erent tools available, such as:

Tabnine: This is a paid code completion tool that suggests best practices, improves readability, and 몭xes errors. Tabnine is compatible with more than 20 programming languages and 15 code editors, such as VS Code, IntelliJ, Android Studio, and Vim. A team of 3 developers can use it for $432 per year.

CodeT5: This is an open-source programming language model that uses Google’s T5 framework to generate code based on natural language descriptions. CodeT5 can complete functions given the target function name and simplify complex code. It supports 7 languages, including Ruby, JavaScript, Go, Python, PHP, C, and C#. CodeT5 is a free tool that can be accessed through Google Colab

ML-Enhanced Code Completion: This is a code suggestion tool that predicts completion based on surrounding code, using encoder-decoder transformer models. It supports 4 languages, including Java, Python, Go, and TypeScript, and it’s integrated

supports 4 languages, including Java, Python, Go, and TypeScript, and it’s integrated with Google Cloud Shell Editor.

Future Developments and Potential Applications

Here are some potential developments and applications that could be expected from these AI-based code-generation tools in the future:

Improving the accuracy and relevance of code suggestions: Generating code suggestions that accurately and relevantly match user expectations and requirements remains a challenge for tools like GitHub Copilot and ChatGPT. This challenge could be addressed by incorporating user feedback, using more comprehensive and diverse training data, applying quality control mechanisms, and developing robust evaluation metrics.

Supporting a wider range of programming languages: Another challenge that needs to be tackled is the support of a wider range of programming languages and frameworks that may not be well-covered in the training data This could be done by using more language-speci몭c data sources, leveraging cross-lingual transfer learning, and developing language-agnostic models.

Enhancing integration with code editors and platforms: Integrating well with various code editors and platforms used by developers is another challenge that requires attention. Achieving this could be done by using standardized protocols, such as the LSP, developing more compatible extensions and plugins, and collaborating with other tool providers and communities.

Exploring new use cases and scenarios for code generation: The current scope of the tools like GitHub Copilot and ChatGPT is limited, so exploring new use cases and scenarios for code generation is a challenge. Identifying new problems and opportunities developers face, developing new features and functionalities to address them, and testing them with real users and feedback could help overcome this challenge.

Conclusion

GitHub Copilot and ChatGPT are both AI-powered tools that can help you write code faster and with less e몭ort. However, they have di몭erent features, strengths, and weaknesses that make them appropriate for di몭erent scenarios and use cases.

In this article, we have compared GitHub Copilot and ChatGPT in terms of their features, performance, usability, compatibility, customization, privacy, data sources, bias, pros and

performance, usability, compatibility, customization, privacy, data sources, bias, pros and cons, use cases, scenarios, and future developments.

GitHub Copilot is preferred and recommended for professional developers who want to write code faster and with less e몭ort. ChatGPT is preferred and recommended for beginners and users learning to code who want to generate code with explanations and perform other natural language tasks.

If you are curious if ChatGPT will eventually replace programmers altogether, I recommend checking out this post: Will ChatGPT Replace Programmers? What You Need To Know.

I hope you enjoyed reading this post and learned something new If you have any questions or feedback, please feel free to leave a comment below. Thank you for your time and attention!

FAQs

Here are some of the frequently asked questions about GitHub Copilot and ChatGPT:

Q: What is GitHub Copilot?

A: GitHub Copilot is an AI-powered code completion tool that suggests code based on your context and intent. It o몭ers multi-line function suggestions, speeds up test generation, supports many programming languages, integrates with various code editors and platforms, and has a transparent privacy policy and data usage policy.

Q: What is ChatGPT?

A: ChatGPT is an AI-powered language model that generates text based on natural language input It generates code for di몭erent programming languages, performs tasks such as translation and summarization, chats with you across multiple languages and works as an online service that you can access through a web browser or a mobile app.

Q: How much does GitHub Copilot cost?

A: GitHub Copilot costs $10 per month for individuals and $19 per month for business users. You can also try it for free for 30 days.

Q: How much does ChatGPT cost?

A: ChatGPT is free for anyone who wants to try it out. You can also upgrade to a premium subscription ($20 per month) for more features.

Q: Is Copilot the same as ChatGPT?

A: No, Copilot and ChatGPT are not the same. They are di몭erent tools that use di몭erent AI models and have di몭erent functionalities Copilot is an AI pair programmer that helps you write code, while ChatGPT is an AI chatbot that helps you generate natural language.

Q: Which is better GitHub Copilot or ChatGPT?

A: It’s di몭cult to give a conclusive answer to this question because both tools have their unique strengths and limitations. GitHub Copilot is an AI-powered programming partner that assists in accelerating your coding process with less e몭ort. In contrast, ChatGPT is an AI chatbot that can produce natural language outputs based on a given input. Your preference and requirements determine which tool suits you best

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