AI coding assistants explained: An in-depth look at GitHub Copilot

When considering the role of an assistant for software developers, important questions arise: Should this assistant possess greater experience, enhanced speed, or both? GitHub Copilot aims to deliver on all fronts by acting as an AI-powered coding assistant that boosts productivity without sacrificing code quality.

This article examines GitHub Copilot’s capabilities as a coding assistant. It provides a detailed overview of its key features, practical usage tips, and relevant considerations for integration into daily development workflows. Additionally, the discussion addresses the broader implications of adopting AI-assisted coding tools, emphasizing factors developers should evaluate to effectively leverage GitHub Copilot in professional environments.

Ideal coding assistant

Before examining GitHub Copilot in detail, it is important to consider what qualities an effective coding assistant should possess.

An ideal assistant would combine both experience and speed. More specifically, it should demonstrate the following characteristics:

1. Judgmentally silent: A competent assistant offers recommendations without imposing decisions, allowing developers to retain control over their work.
2. Context-aware: The assistant must understand the codebase, system architecture, and the development team’s established conventions.
3. Unobtrusively fast: While speed is important, it should be accompanied by relevant and accurate suggestions.

What is an AI coding assistant

Everyone is familiar with the peer-to-peer programming approach and its power in solving complex problems when two heads work together to achieve the best results. In the era of AI, that seems to become a habit. AI assisted coding is already happening, it works and give results. AI-Assisted Development Tools are right now empowering developers by accelerating routine tasks and enhancing code quality.

Nowadays, AI coding assistants are assimilated with tools (IDEs or extensions of existing IDEs) that help developers producing code faster. Under the hood there’s more, and AI is basically helping the developers writing more performant code in alignment with the industry standards.

There are already many options when it comes to choosing an AI coding assistant, but there are two that stand out from the others:

GitHub Copilot powered by GitHub and OpenAI

• It is an AI code completion tool for most of the existing code editors (VS Code, Visual Studio, JetBrains IDEs, etc.).
• The main purpose is to suggest code, functions, and boilerplate as you type in your IDE.
• It’s best for boosting productivity, autocompleting code, and writing functions quickly.
• The key strength is seamless inline suggestions and comment-driven code generation.

Cursor developed by Anysphere Inc

• It is a full AI-first code editor, blending Copilot-like features with GPT-style chat and codebase awareness. In fact, it is a code editor (based on VS Code) with deeply integrated AI.
• It combines code editing with AI chat, inline code edits, and context-aware suggestions.
• It’s best for refactoring, debugging, exploring large codebases, and asking coding questions. It includes a built-in Copilot-style suggestions plus a chat assistant side panel.
• The key strength is that you can “ask” the entire codebase questions, make AI-driven changes inline, and use GPT in the IDE.

There are many other AI coding assistants (like Tabnine, Replit, Amazon CodeWhisperer, etc.), but in this article, we will be focusing only on the GitHub Copilot.

GitHub Copilot

GitHub Copilot, developed by GitHub in collaboration with OpenAI, is a tool designed for code completion, generation, and analysis. It supports users of several integrated development environments (IDEs), including Visual Studio Code, Visual Studio, Neovim, and JetBrains IDEs, by providing code autocompletion and related functionalities.

Powered by machine learning models such as Codex, which is based on GPT technology, GitHub Copilot offers code suggestions in real time as developers type. Trained on billions of lines of publicly available code, it supports a wide range of programming languages and frameworks. Copilot functions as an “AI pair programmer,” helping reduce repetitive coding tasks and improve overall productivity.

The primary features and uses of GitHub Copilot include:

1. Code completion and suggestions: Provides real-time suggestions ranging from single lines of code to complete functions. This is particularly useful for generating boilerplate code, common algorithms, or configuration files.
2. Context-aware code generation: Understands the context of the project by analyzing variable names, comments, and function signatures. For example, a comment like “// retrieve user by ID from database” can prompt Copilot to generate a corresponding function.
3. Language and framework versatility: Supports a broad spectrum of programming languages, including JavaScript, Python, TypeScript, Go, Ruby, C#, and Java. It also integrates effectively with popular frameworks such as React, Angular, Django, ASP.NET, and Node.js.
4. Learning and exploration: Assists developers in learning new languages or APIs by providing idiomatic code examples. It serves as an interactive reference, reducing the need to consult external documentation or search engines.
5. Test generation (partial support): Can help scaffold unit tests based on existing code or descriptive comments. This feature is beneficial for test-driven development (TDD) workflows and for adding tests to legacy codebases.

Brief overview of GitHub Copilot functions

GitHub Copilot continues to evolve, gaining new features and expanding its capabilities. Currently, the most common and recognized functionalities include:

1. Code completion and generation

• Autocompletes entire functions based on function names and brief comments.
• Provides inline code suggestions while typing, particularly for loops, conditional statements, and API calls.
• Accelerates creation of repetitive code such as data transfer objects (DTOs), serializers, and data classes.

2. Boilerplate and scaffolding

• Generates common code patterns, including REST API routes, database queries, and form validations.
• Assists in building user interface components by suggesting HTML/JSX structures, CSS classes, and event handlers for frameworks like React and Angular.
• Helps create configuration files such as .gitignore, Dockerfile, and package.json.

3. Test case generation

• Automatically generates unit test stubs based on method names, supporting popular testing frameworks such as xUnit, JUnit, and Jest.
• Suggests edge cases that may be overlooked during manual test design.
• Produces mocks and stubs for services, repositories, and API calls.

4. Comment-driven development

• Converts descriptive comments (e.g., // fetch user by ID from API) into corresponding code snippets.
• Supports this feature across multiple programming languages including Python, JavaScript, TypeScript, C#, and Go.

5. Code refactoring assistance

• Recommends improved coding patterns, such as converting imperative code to use LINQ, map/reduce, or asynchronous constructs.
• Suggests breaking down lengthy methods into smaller, logically grouped helper functions.
• Offers alternative method signatures and restructuring suggestions, although it does not perform full refactoring like traditional IDE tools.

6. Learning and code explanation

• While not explicitly designed to explain code, Copilot can generate summaries or explanations when prompted by comments such as // Explain what this function does.
• This functionality is useful for onboarding new team members or understanding legacy code.

7. Language and framework translations

• Facilitates migration of code patterns between programming languages, for example, converting JavaScript functions to Python equivalents.
• Assists in adapting frameworks, such as transforming Express.js routes into FastAPI counterparts, based on user prompts.

8. Writing simple DevOps files

• Enables rapid creation of GitHub Actions workflows, initiated by comments like # deploy to Netlify on push.
• Supports generation of basic configuration files for Docker and Kubernetes environments.

Limitations of GitHub Copilot

• It does not provide detailed explanations regarding why certain code functions as it does.
• It is not suited for extensive refactoring tasks involving multiple files or major architectural changes.
• Its contextual understanding is limited to the currently open file and a few adjacent files, restricting broader project awareness.

Licensing considerations

As with any software tool, GitHub Copilot operates under specific terms and conditions, which are influenced by the type of license selected by the user.

Privacy and compliance are key concerns when using AI-based coding tools. Common questions include:

• Will my code be used to train other large language models (LLMs)?
• Is the code generated by GitHub Copilot compliant with the licensing requirements of my software?

To address these concerns, it is important to understand the underlying structure and operation of GitHub Copilot:

• GitHub Copilot is based on models pre-trained using publicly available code, such as repositories accessible on GitHub.
• User code is not sent back to retrain these models during usage.
• Some code snippets may be transmitted to GitHub’s servers to facilitate suggestion generation.

For organizations working on commercial software, GitHub Copilot offers two specific license types designed to provide additional assurances:

• Business
• Enterprise

Both licenses include what GitHub refers to as the “Copyright Shield,” which aims to safeguard users by ensuring compliance and protecting against copyright-related risks.

Awareness points and best practices

While GitHub Copilot is considered safe to use under the appropriate licensing, developers should remain mindful of several important considerations:

1. Avoid exposure of sensitive data: Refrain from using Copilot or similar AI assistants to process sensitive information, including API keys, proprietary algorithms, credentials, or customer data.
2. Review generated code thoroughly: AI-generated suggestions may occasionally be incorrect, inefficient, or present security vulnerabilities. It is essential to carefully review and test all AI-generated code, especially when working on critical systems.
3. License ambiguity: Copilot may sometimes produce code snippets resembling open-source content subject to restrictive licenses such as the GPL. When developing commercial or proprietary software, exercise caution to avoid potential licensing conflicts.
4. Compliance and data residency: In regulated industries such as healthcare and finance, ensure that AI tool usage complies with data residency requirements and organizational security policies.
5. Limit AI role in domain logic: While AI tools can effectively handle boilerplate code, refactoring, and testing, they should not replace domain experts or the design of complex business logic.

To maximize the effective and responsible use of AI coding assistants, consider the following best practices:

• Remember that AI suggestions are recommendations rather than automatic completions; responsibility for reviewing and approving generated code remains with the developer.

• Disable telemetry and logging features if necessary to reduce external exposure of your code.

• Develop and maintain comprehensive tests for AI-generated code to identify defects or unintended behaviors early in the development cycle.

• Use comments to clearly document your code’s intent, as AI may not always interpret complex or nuanced scenarios accurately.

• Integrate AI tools into your code review processes as a supplementary aid, while relying on human judgment for final decisions.

AI as a developer superpower

The question often arises: is AI the ultimate game-changer for developers?

While the answer is nuanced, one aspect is clear: AI significantly enhances developer capabilities. It is not intended to replace developers but rather to serve as a valuable tool within their workflow.

1. Speed and efficiency: AI can generate boilerplate code, test stubs, SQL queries, and configuration files in seconds. Tasks that previously required minutes or hours can now be completed much faster, enabling developers to focus on design, architecture, and problem-solving rather than repetitive syntax or routine coding.
2. Instant knowledge: AI provides immediate examples and explanations for unfamiliar APIs, frameworks, or programming languages. This functionality acts as an integrated reference, offering developers guidance without leaving their development environment.
3. Enhanced debugging and refactoring: AI tools can analyze errors, propose fixes, and assist with refactoring large portions of code. This capability is particularly beneficial when working with legacy systems or extensive codebases.
4. Accelerated learning: For less experienced developers, AI can facilitate faster learning by identifying coding patterns, responding to queries, and providing contextual insights. This interactive approach can be more effective than solely relying on static documentation.
5. Creative exploration: Developers can use AI to prototype multiple solutions, explore different approaches to problems, and quickly test ideas. This supports brainstorming and problem-solving, especially during challenging development phases.

Despite these advantages, AI tools have limitations:

• Rapid code generation can introduce incorrect or insecure logic.
• Broad familiarity with APIs does not equate to deep domain knowledge.
• Increased productivity may lead to over-reliance or superficial coding practices.
• AI-generated explanations can occasionally be inaccurate or misleading.

AI serves as a powerful asset for developers who maintain critical thinking, do not accept suggestions uncritically, and use AI to automate routine tasks. It is essential to prioritize human judgment, domain expertise, and creativity in conjunction with AI capabilities.

Bottom line

In the context of software development, AI – particularly GitHub Copilot- serves as a tool to enhance developer efficiency and productivity. It is designed to augment the development process rather than replace the effort involved.

Ultimately, developers remain responsible for ensuring the quality, security, and compliance of the code they produce.