Github Copilot

Author: Andreas Abrahamsen, Vicktoria A. Bure, Andreas S. Joakimsen

Introduction
History of GitHub Copilot
Main Features
Strengths and Weaknesses
Market Comparison
Getting Started
Conclusion

Introduction

Github Copilot is a coding assistant powered by Artificial Intelligence. It is developed by Github and OpenAI. GitHub Copilot offers suggestions that are aware of the context, from single lines of code to complete functions. It is available as an extension for Visual Studio Code, Visual Studio, Neovim, JetBrains IDEs, and Azure Data Studio, and offers support for a wide range of different languages.

Designed as an “AI pair programmer,” Copilot helps developers write code faster by suggesting names for variables, type annotations, and unit tests. It boosts productivity but it is meant to assist, not replace, developers, and act as a collaborative partner when coding.

History of GitHub Copilot

2020 (June): OpenAI released GPT-3, a large language model demonstrating the potential for AI to generate programming code alongside natural language. This sparked interest at GitHub, which partnered with OpenAI to create GitHub Copilot, an AI tool designed to assist developers with context-aware code generation.
2021 (June): GitHub Copilot debuted in a technical preview for Visual Studio Code, powered by OpenAI’s Codex model. Codex, a fine-tuned version of GPT-3 trained on billions of lines of public code, enabled Copilot to suggest code snippets, complete functions, and adapt to a variety of programming languages.
2021 (October): Copilot expanded to Neovim and JetBrains IDEs, opening its functionality to developers using lightweight and professional development environments. This broadened its adoption among a diverse developer audience.
2022 (March): Support was added for Visual Studio 2022, enabling enterprise developers to integrate Copilot into their workflows. This marked a step toward making the tool enterprise-ready.
2022 (June): GitHub Copilot exited its technical preview and became widely available as a subscription-based service. Individual developers could now subscribe, while students and verified open-source contributors received free access. It quickly became popular for its ability to reduce repetitive coding tasks and help developers learn new languages or frameworks.
2023 (March): GitHub announced Copilot X , a significant upgrade powered by OpenAI’s GPT-4 model. The update introduced:
- Conversational Capabilities: Developers could interact with Copilot through chat or voice to resolve issues, clarify code, or generate new snippets.
- Pull Request Integration: Copilot provided suggestions for code reviews and improvements directly within GitHub pull request workflows.
- Command Line Assistance: AI support extended to terminal tasks.
- Documentation Support: Copilot could generate or improve project documentation, further simplifying developer workflows.
- Enterprise Focus: GitHub introduced enterprise-specific features, such as centralized management for licensing, enhanced security, and tools to support team collaboration. These features allowed businesses to adopt Copilot at scale while maintaining governance over its use.
2024 Enhancements:
- Copilot Extensions Guide and Compliance Reports (June 2024): GitHub released tools and documentation for developers to build custom Copilot Extensions, promoting personalization and integration within workflows. GitHub released SOC 2 Type I and ISO 27001 compliance reports for Copilot Business and Enterprise, ensuring enterprise-level security and trust.
- Enhanced Copilot Chat (July 2024): Copilot Chat was upgraded with OpenAI’s GPT-4o, delivering faster and more accurate interactions across platforms like Visual Studio, VS Code, JetBrains IDEs, GitHub Mobile apps, and GitHub.com.
- Multi-Model Support and GitHub Spark Introduction(October 2024): Copilot began offering support for multiple AI models from providers like Anthropic, Google, and OpenAI, giving developers flexibility to choose the most suitable model for their needs. GitHub introduced Spark, an AI tool for building web applications using natural language prompts, powered by OpenAI and Anthropic models.
Impact: Copilot transformed software development by streamlining repetitive tasks, accelerating learning for junior developers, and providing advanced debugging and testing support. It also highlighted ethical concerns about AI’s use of publicly available code, prompting ongoing discussions about responsible AI use.
Future Directions: GitHub plans to expand Copilot’s capabilities with deeper integrations into GitHub Actions, AI-driven debugging tools, and support for software architecture design. These advancements aim to solidify Copilot as a cornerstone of the modern development ecosystem.

Main Features

GitHub Copilot gives a wide range of features that improve developer productivity and streamline the coding process. Below is a list of its main features.

Feature	Description
Code Completion	Offers intelligent, autocomplete-style recommendations in compatible IDEs, including Vim/Neovim, Visual Studio, Code, and jetBrains IDEs
Pull Request Summaries	This is an AI-generated summary of changes that are made in pull requests, highlighting affected files and giving key points for reviewers. (Enterprise-only feature.)
Text Completion (Beta)	This Helps to generate an accurate pull request description quickly, helping with both time and consistency. (Enterprise-only feature.)
Knowledge Bases	Allows a user to define documentation as context when using Copilot Chat. This results in more precise answers. (Enterprise-only feature.)
Suggestions for Unit Tests	Recommends simple unit tests and code examples to strengthen the quality of the code.
Integration with IDEs	Easily combined into multiple IDEs, making sure the developers can use Copilot without disorganizing their existing workflows.

Strengths and Weaknesses

Strengths

Faster Coding

Code Suggestions: Predicts and writes code for you, saving time on tedious tasks.
Handles Complexity: Generates algorithms, data structures, and features from simple comments.
Less Searching: Reduces time spent searching for snippets or documentation.

Broad Support

Versatile: Works with many programming languages and frameworks.
Context-Aware: Adapts to the language and framework you are using.

Awareness

Understands Code: Provides suggestions based on the current file and its context.
Natural Language: Generates code from comments like “function to calculate days.”

Improves Code Quality

Best Practices: Suggests solutions that follow community standards.
Fewer Errors: Minimizes common mistakes like typos or incorrect API usage.

User-Friendly

Easy to Use: Integrates seamlessly with tools like Visual Studio Code.
Control Options: Accept, reject, or modify suggestions as needed.

Learning Tool

Teaches Coding: Helps new developers learn patterns and syntax.
Encourages Creativity: Offers alternative approaches to problem-solving.

Weaknesses

Context Limitations

Limited Scope: Only understands visible code, missing dependencies in other files.
Oversimplifies Tasks: May struggle with large or complex projects.

Code Quality Issues

Not Always Optimal: Suggestions might work but are not always efficient.
Misses Edge Cases: May fail to address unique or non-standard requirements.

Security Risks

Insecure Patterns: Could suggest code with security vulnerabilities (e.g., SQL injection).
Needs Review: Developers must verify suggestions for safety and compliance.

Training Limitations

Generic Suggestions: Based on public GitHub code, which may not suit specialized needs.
Outdated Practices: Might suggest older coding methods.

Legal Concerns

Copyright Issues: May inadvertently reproduce copyrighted code.
Plagiarism Risk: Generated snippets could conflict with licensing restrictions.

Over-Reliance

Less Learning: Developers might not fully understand auto-generated code.
Debugging Problems: Fixing issues in unfamiliar code can be challenging.

Specialization Gaps

Weak in Niche Areas: Struggles with unique languages or cutting-edge technologies.
Limited Customization: Difficult to adapt to highly specific workflows.

Resource Heavy

Performance Issues: Requires a stable internet connection and robust hardware.
Occasional Lag: Complex tasks may slow down suggestions.

Market Comparison

While there are many contenders to first place, there is a broad consensus that Tabnine is the strongest competitor. This segment will focus on just that, and will take a closer look at key features such as language support, integration, AI capabilities and specific strengths, and will close with a short comparison wrap-up.

Tabnine vs. GitHub Copilot

AI-model: Copilot uses a powerful,tranformer-based model of open AI called the codex-model. Tabnine, while still robust, is often a bit more dependent on smaller models, or a local-first approach. This results in it not being as resonating as Copilot.
Privacy: For the developers that value privacy; Tabnine offers a local model option, while Copilot uses Microsoft/cloud servers to process the code suggestions. If you are part of a team that needs strict data privacy, Tabnine seems to be the better option. This may be the reason for the launch of Copilot X. As the concern grew bigger, Github addressed their concerns by publishing information on how they handle data, with focus on protecting the users code, and personal data. At the same time , there have been studies, proving that code generated by Copilot, might contain some weaknesses when it comes to security. One study showed that some code examples generated by Copilot contained some security problems. Even though Github has attempted to solve these problems, there are still discussions on whether Copilot X solves all the security issues. Therefore it is highly recommended that every developer using Copilot, stays vigilant, and examines their code, and always follow best practices, when using Copilot.
Integrasjon: Tabnine supports a wider range of IDEs than Copilot, such as VS code, JetBrains, Sublime Text, Vim, Atom and more. This makes it more flexible for developers that use less common tools.
Language support: Like copilot, Tabnine supports the use of multiple languages, but it also provides the opportunity to specialise in different languages or frameworks, depending on the developers’ needs.
Cost and availability: Tabnine offers a free version, which makes it more available for developers that need more basic functionality without having to pay for it.
Where Copilot Still Leads: GitHub Copilot has an advantage in terms of natural language to code functionality, which is more advanced than Tabnine’s proposal. Copilot also integrates seamlessly with the GitHub ecosystem, which is a big advantage for developers who already use GitHub daily.

Comparison Wrap-Up:

Despite Github Copilot being a versatile and powerful AI tool, Tabnine is a great contender, especially when the developer is in need of privacy and flexible integration. Tabnine is also the better option when talking about cost effectiveness.

Getting Started with GitHub Copilot

1. Requirments:

To start using Github Copilot the user needs the following:

GitHub account (paid or free)
A supported IDE (e.g., vs code, Neovim, jetbrains, etc.)
An active GitHub Copilot subscription or eligibility for free access(student or verified open-source contributors)

2. Enabling GitHub Copilot

Go to the Github Copilot subscription page Github Copilot subscription page and activate a subscription plan.
Students and open-source contributors can apply for free access.

2. Install the extension:

For Visual Studio Code:
1. Open the Extension Marketplace CTRL+Shift+X
2. Search for “GitHub Copilot” and click Install.
3. Once installed, restart your IDE.
For other supported IDEs: Follow the installation instructions provided at Github.

After installation, sign in to your GitHub account through the extension. This step ensures Copilot has access to your account and IDE.

3. Configuring GitHub Copilot

Once installed, configure Copilot to match your preferences:

Enable or Disable suggestions: Choose when Copilot provides suggestions(e.g, on new lines, within existing code etc)
Privacy settings: Decide whether Copilot can use your code snippets to improve its AI models.
Keybindings: Customize shortcuts to accept or reject suggestions quickly.

4. Using GitHub Copilot

1. Start Typing

Begin writing code in a supported file type (e.g., JavaScript, Java or Python). Copilot will automatically suggest completions, ranging from single lines to entire functions.

2. Accepting Suggestions

Press Tab or Enter to accept a suggestion.

3. Exploring Alternative Suggestions

Use Ctrl + ] (or the equivalent keybinding) to cycle through multiple suggestions for the same prompt.

4. Custom Prompts

Write a comment or a plain text descriptions of what you need to guide Copilot. For example:

// Create a JavaScript function to reverse a string
function reverseString(str) {
    return str.split('').reverse().join('');
}

5. Advance features

Context-Aware assistance: Copilot adapts its suggestions based on the surrounding code, project structure and coding patterns.
Comments to guide Copilot: Developers can use plain English comments to guide Copilot, e.g., # Create a function to sort an array.
Real-Time Feedback: Modify Suggestions directly in the IDE and provide feedback to refine future outputs

6. Tips for use of Copilot:

Start small: Begin with tasks like boilerplate code, repetitive patterns, or setup scripts..
Combine with traditional coding: Treat Copilot as a collaborative assistant, not a replacement. Use it to complement your work by handling simpler or repetitive tasks while you focus on complex logic.
Review outputs: Verify AI-generated code for accuracy, logic errors, inefficiencies, or potential vulnerabilities - especially when working on critical or sensitive tasks.

Conclusion

GitHub Copilot has changed the way developers work by acting as a smart assistant that simplifies and speeds up coding. With its ability to understand context, suggest code, and even complete entire functions, Copilot is a powerful tool for both beginners and experienced developers. It integrates seamlessly with popular IDEs like Visual Studio Code, supports many programming languages, and helps developers focus on solving problems instead of repetitive tasks.

However, it’s important to use Copilot thoughtfully. While it excels at routine coding tasks, it may struggle with complex problems, miss edge cases, or suggest outdated practices. Security is another concern—its suggestions should always be reviewed to ensure they are safe and suitable for your project. For privacy-conscious users, alternatives like Tabnine might be worth considering.

Copilot’s strengths lie in making coding faster, easier, and more accessible, but it is not a replacement for developer expertise. It works best as a helper that complements your skills and supports you in creating better code. Whether you’re automating repetitive tasks, brainstorming solutions, or learning something new, Copilot can be an invaluable partner in your workflow.

Looking ahead, GitHub Copilot is set to grow with even more features, like advanced debugging, deeper platform integration, and tools for designing software architecture. As AI in software development evolves, Copilot will remain a key tool to enhance productivity and creativity. In summary, GitHub Copilot is a game-changer for coding, offering speed, support, and inspiration. Use it as a tool to boost your productivity, improve your workflow, and explore new ideas—all while staying vigilant and reviewing its suggestions to ensure quality and security.