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Amazon CodeWhisperer vs. Copilot: Which Should You Choose?

Developers facing tight deadlines often turn to AI coding assistants for support. These tools improve productivity by providing real-time suggestions and streamlining repetitive tasks. But not every tool is equal. In the debate over Amazon CodeWhisperer vs. Copilot from Github, businesses want to know how these tools differ and which is the best choice for their work.

Learn more about the key features, advantages and distinctive characteristics of GitHub Copilot and Amazon CodeWhisperer. We will examine how they integrate with popular development environments, their approaches to privacy and security, and how their underlying models and training data differ. Additionally, you'll gain insights into the limitations of these assistants and learn about their common use cases.

Why Use an AI Coding Assistant? 

Using an AI coding assistant offers several key benefits for developers and teams. First, it improves productivity, saves time and improves efficiency by providing real-time suggestions and automating repetitive tasks. Developers can code faster while redirecting their focus to the strategic and critical aspects of their projects.

Second, AI coding assistants improve code quality by offering suggestions, catching potential errors and promoting best practices. The result is cleaner, more reliable code that's less prone to bugs.

Third, these assistants also contribute to employee learning and skill development. Developers receive insights and suggestions to improve their work, expand their knowledge and follow best practices. 

Overview of Copilot vs. CodeWhisperer

Here’s an overview of each AI coding assistant, along with key differences between the two development tools.

Amazon CodeWhisperer

Amazon CodeWhisperer is an AI coding assistant developed by Amazon. It's designed to provide real-time, contextually relevant code suggestions to developers directly within their integrated development environment (IDE). CodeWhisperer can accelerate software development by offering tailored code suggestions, comment completions and security scans to detect vulnerabilities. It supports a range of popular programming languages and is compatible with various IDEs, including Visual Studio Code and JetBrains IDEs.

CodeWhisperer and AWS go hand in hand. Users receive code suggestions for AWS application programming interfaces (APIs), making it even more valuable for developers on AWS projects.

CodeWhisperer is trained on billions of lines of code, both publicly available and exclusive to Amazon, to provide accurate and helpful code suggestions. It emphasizes data security by encrypting data in transit and at rest. Users can take control of data retention and telemetry data collection. Additionally, CodeWhisperer integrates with Amazon Q, a chat-style tool for AWS powered by Amazon Bedrock. Amazon Q gives developers a chat interface within their IDE, further improving their AWS development experience.

GitHub Copilot

Github Copilot was developed through a collaboration between GitHub, OpenAI and Microsoft. It offers advanced functionalities, not just auto-complete suggestions. Trained on billions of lines of code from public repositories, Copilot can generate code based on the provided context, including entire lines or blocks, to improve developer efficiency. This tool also supports multiple programming languages and frameworks.

Copilot integrates seamlessly with popular IDEs and code editors, including Azure Data Studio, Visual Studio and JetBrains IDEs, allowing developers to work in their preferred coding environment. Copilot also offers a chatbot tool called Copilot Chat, which allows direct interaction and assistance with tasks such as generating unit tests, explaining code and suggesting bug fixes. Users are advised to review code suggestions, as there may be instances where the generated code is semantically or syntactically incorrect.

Key Differences Between CodeWhisperer and Copilot

When it comes down to Amazon CodeWhisperer vs. Copilot, which is the best AI coding assistant for your business? Start by examining key differences between the two offerings. While both tools aim to improve the coding experience and improve developer efficiency, they have distinct features and capabilities.

CodeWhisperer specializes in AWS cloud development, providing tailored guidance and suggestions for AWS-specific coding and infrastructure. It’s especially beneficial for developers building applications within the AWS ecosystem. Copilot, meanwhile, is a more general-purpose AI coding assistant that handles a wide range of programming tasks across languages and frameworks. While Copilot offers flexibility and a large number of languages supported, it’s not specifically tailored to any particular technology or platform.

Copilot supports an impressive range of languages, including with code generation. CodeWhisperer, while supporting fewer languages, focuses on the most commonly used languages in AWS development, providing developers with support for their AWS projects.

Integration and ecosystem also differ between the two tools. Copilot can be seamlessly integrated into various code editors and IDEs, making it accessible to a wide range of developers. While offering less overall range, CodeWhisperer and AWS are tightly integrated. Developers can leverage the full power of AWS services with CodeWhisperer by their side.

CodeWhisperer vs Copilot

8 Use Cases for AWS CodeWhisperer

Let’s take a look at eight use cases that demonstrate the power of CodeWhisperer as a go-to generative AI coding assistant.

Inline Code Suggestions

CodeWhisperer can analyze developer code as it’s written, with real-time suggestions for completing the current line or generating the next line. These suggestions are based on existing code and the desired outcome, making it easier for developers to write code that aligns with their intentions.

For example, when writing a function, CodeWhisperer can suggest the parameters, return types and even the function implementation based on context. This saves developers from manually typing out the entire function signature and implementation. They can instead focus on the core logic of their code.

CodeWhisperer's inline code suggestions aren't limited to just completing code snippets. This AI-powered assistant can also help optimize code, improve developer performance and reinforce best practices. With CodeWhisperer, developers can write efficient and high-quality code without having to spend unnecessary time researching or consulting external resources.

Autocomplete and Debugging

When developers debug their code, they often rely on console.log statements to print out values and debug information. CodeWhisperer can offer relevant autocomplete suggestions for console.log statements in JavaScript.

As developers start typing the console.log statement, CodeWhisperer analyzes the code context and suggests variables, objects or expressions that can be printed to the console. This saves developers from manual work while reducing the risk of typos or syntax errors.

CodeWhisperer can provide suggestions for method names, function calls, variable names and more. This is particularly useful when working with complex APIs or libraries, as CodeWhisperer can suggest the available methods and properties based on the imported modules or objects.

Infrastructure Modernization

Dockerization is a popular approach that allows applications to be packaged into containers, providing consistency and portability across environments. CodeWhisperer can generate code snippets and provide suggestions for Dockerizing applications. The tool can also generate Dockerfile templates, suggest the appropriate base images and provide guidance on configuring the container environment.

CodeWhisperer can also help with infrastructure setup using AWS CDK, a powerful tool for defining cloud infrastructure as code. CodeWhisperer can generate code snippets and provide guidance when using AWS CDK for infrastructure components such as Amazon EC2 instances, Amazon S3 buckets or AWS Lambda functions. The AI coding assistant also can suggest the appropriate constructs, provide examples of how to define resources and offer best practices for structuring infrastructure code.

Code Reviews and Bug Detection

During a code review, CodeWhisperer can identify potential issues or areas of improvement. It can detect common coding mistakes, such as syntax errors, incorrect variable assignments, or unused variables. It can also identify code smells, such as duplicated code, long methods or complex control flow, and suggest refactoring techniques to address these issues.

In addition to code quality, CodeWhisperer can improve bug detection by identifying difficult-to-spot bugs or logical errors that might go unnoticed during manual code reviews. Examples include off-by-one errors, null pointer dereferences or race conditions. CodeWhisperer can provide suggestions for fixing these bugs or offer ways to prevent them.

CodeWhisperer can also check for adherence to style guidelines, naming conventions and architectural patterns, which reinforces best practices and coding standards during code reviews.

Code Optimization and Refactoring

CodeWhisperer can analyze code snippets and provide suggestions for improving performance or memory usage. It can identify inefficient algorithms, redundant computations or resource-intensive operations, as well as offer alternative approaches to optimize the code. The assistant can suggest more efficient algorithms, data structures or coding patterns that can significantly improve the performance of the code.

CodeWhisperer can also analyze the codebase and identify areas to apply refactoring. It can suggest code transformations, such as extracting methods, renaming variables or simplifying complex expressions, to make the code more modular and easier to understand. These refactoring suggestions can help developers improve ‌overall code structure and maintainability, reduce technical debt and make future modifications easier.

Documentation Assistance

CodeWhisperer can generate documentation based on code comments, function signatures and other code annotations. This documentation can be formatted in Markdown, HTML or plain text. This use case saves developers time and effort in manually writing documentation, especially for large or complex projects.

CodeWhisperer can also help update documentation by matching code changes to corresponding sections in documentation that should be updated. It can suggest specific changes or additions to sync documentation with the codebase, all in accordance with your large language model (LLM) policy

Natural Language Queries

CodeWhisperer is designed to understand natural language queries and respond in a conversational manner. Developers can ask questions like "How do I implement a login feature in Python?" or "What's the best way to sort an array in Java?" and CodeWhisperer will provide relevant suggestions and guidance.

When a natural language query is received, CodeWhisperer extracts key information, such as the programming language or desired functionality, to understand the developer’s intent. CodeWhisperer then provides tailored suggestions, code snippets or explanations to help developers achieve their desired outcome.

CodeWhisperer can also conversationally handle follow-up questions or clarifications. Developers can ask for more details, alternatives or additional examples. CodeWhisperer will continue the conversation while providing updated information and guidance.

Learning and Tutorials

When developers face challenges while coding or have questions about programming concepts, syntax or best practices, CodeWhisperer can act as a personal guide.

It can offer explanations and examples for numerous programming concepts, helping developers understand not only the immediate answer but the underlying principles. CodeWhisperer can also break down complex topics and provide step-by-step guidance for solving coding problems.

CodeWhisperer can also provide real-time feedback on code snippets or solutions, which helps developers learn from their mistakes and improve their coding skills. Your developers essentially receive professional development while completing business-critical projects.

8 Use Cases for AWS CodeWhisperer

Closing Thoughts

When it comes to Amazon CodeWhisperer vs. Copilot, you have a choice of two powerful AI-powered assistants that support developer productivity and offer real-time coding suggestions and analysis. If you need an assistant for any number of coding languages and frameworks, Copilot might offer the most versatility. However, if you’re working extensively within the AWS ecosystem, CodeWhisperer is the clear winner.

Developers can leverage the full power of AWS in Amazon CodeWhisperer’s deep integrations. Moreover, the AI coding assistant offers an extensive suite of resources and support to help you improve coding productivity, quality and ROI.

CodeWhisperer is just one of the many ways you can use genAI with AWS. Explore the endless possibilities for your business with AWS and Mission Cloud. 



What is the learning curve associated with integrating these tools into an existing development workflow, especially for teams not previously exposed to AI-assisted coding?

The learning curve for integrating Amazon CodeWhisperer or GitHub Copilot into an existing development workflow can vary. These tools are generally user-friendly, but the extent to which a team is accustomed to AI-assisted coding can affect the adaptation process. Teams new to AI-assisted coding might require some time to familiarize themselves with the tool's capabilities and best practices for integrating AI suggestions effectively into their coding practices.

How do Amazon CodeWhisperer and GitHub Copilot adapt to different programming languages or frameworks, and is their performance consistent across them?

Integrating Amazon CodeWhisperer or GitHub Copilot with development tools and CI/CD pipelines can significantly streamline the software development lifecycle. These AI assistants can enhance coding efficiency, reduce boilerplate code generation time, and identify coding errors early, potentially leading to faster development cycles and more robust codebases.

How do Amazon CodeWhisperer and GitHub Copilot handle proprietary codebases, and what measures are in place to prevent code leakage or unintended sharing of sensitive information?

Concerning proprietary codebases, both tools are designed with mechanisms to respect code privacy and security. They include features to prevent the unintended sharing of sensitive information, ensuring that AI does not compromise the confidentiality of proprietary code. However, developers and organizations should review these tools' privacy policies and security measures to understand how their code is processed and ensure it aligns with their security standards.

Author Spotlight:

Chris Yommer

Senior AppDev Solutions Architect

Andrew Hunton

Senior Software Engineer

Cory Martin

Senior Software Engineer

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