AWS DevOps & Developer Productivity Blog

Optimize software development with Amazon CodeWhisperer

Businesses differentiate themselves by delivering new capabilities to their customers faster. They must leverage automation to accelerate their software development by optimizing code quality, improving performance, and ensuring their software meets security/compliance requirements. Trained on billions of lines of Amazon and open-source code, Amazon CodeWhisperer is an AI coding companion that helps developers write code by generating real-time whole-line and full-function code suggestions in their IDEs. Amazon CodeWhisperer has two tiers: the individual tier is free for individual use, and the professional tier provides administrative capabilities for organizations seeking to grant their developers access to CW. This blog provides a high-level overview of how developers can use CodeWhisperer.

Getting Started

Getting started with CodeWhisperer is straightforward and documented here. After setup, CodeWhisperer integrates with the IDE and provides code suggestions based on comments written in the IDE. Use TAB to accept a suggestion, ESC to reject the suggestion ALT+C (Windows)/Option + C(MAC) to force a suggestion, and left and right arrow keys to switch between suggestions.

CodeWhisperer supports code generation for 15 programming languages. CodeWhisperer can be used in various IDEs like Amazon Sagemaker Studio, Visual Studio Code, AWS Cloud9, AWS Lambda and many JetBrains IDEs. Refer to the Amazon CodeWhisperer documentation for the latest updates on supported languages and IDEs.

Contextual Code Suggestions

CodeWhisperer continuously examines code and comments for contextual code suggestions. It will generate code snippets using this contextual information and the location of your cursor. Illustrated below is an example of a code suggestion from inline comments in Visual Studio Code that demonstrates how CodeWhisperer can provide context-specific code suggestions without requiring the user to manually replace variables or parameters. In the comment, the file and Amazon Simple Storage Service (Amazon S3) bucket are specified, and CodeWhisperer uses this context to suggest relevant code.

Image depicts a person typing on a computer keyboard, with a code editor window on the screen. The code shows a function for uploading a file from a local directory to an Amazon S3 bucket

CodeWhisperer also supports and recommends writing declarative code and procedural code, such as shell scripting and query languages. The following example shows how CodeWhisperer recommend the blocks of code in a shell script to loop through servers to execute the hostname command and save their response to an output file.

Image is a gif of a person typing on a computer keyboard, with a terminal window on the screen displaying a shell script named 'shell_script.sh.' The code defines a list of servers and outputs the file path. As the person types, the code updates with the output path displayed below.

In the following example, based on the comment, CodeWhisperer suggests Structured Query Language (SQL) code for using common table expression.

"Image is a gif of a person typing on a computer keyboard, with a code editor window on the screen displaying a SQL query. The query uses common table expressions to find the age of a product from an inventory table. As the person types, the query updates with the output displayed below in the form of SQL code. The background is a blurred office environment

CodeWhisperer works with popular Integrated Development Environments (IDEs), for more information on IDE’s supported please refer to CodeWhisperer’s documentation. Illustrated below is CodeWhisperer integrated with AWS Lambda console.

"Image is a gif of a person typing on a computer keyboard, with an AWS Lambda console on the screen. The person is entering a prompt to list all the Amazon S3 buckets. As the person types, the console updates with the output code displayed below, which can be executed to show all the S3 buckets."

Amazon CodeWhisperer is a versatile AI coding assistant that can aid in a variety of tasks, including AWS-related tasks and API integrations, as well as external (non AWS) API integrations. For example, illustrated below is CodeWhisperer suggesting code for Twilio’s APIs.

"Image is a gif of a person typing on a computer keyboard, with an integrated development environment (IDE) on the screen. The person is entering a prompt to write a code that uses the Twilio API to make a voice call. As the person types, the IDE updates with the output function displayed below, which can be executed to make the voice call."

Now that we have seen how CodeWhisperer can help with writing code faster, the next section explores how to use AI responsibly.

Use AI responsibly

Developers often leverage open-source code, however run into challenges of license attribution such as attributing the original authors or maintaining the license text. The challenge lies in properly identifying and attributing the relevant open-source components used within a project. With the abundance of open-source libraries and frameworks available, it can be time-consuming and complex to track and attribute each piece of code accurately. Failure to meet the license attribution requirements can result in legal issues, violation of intellectual property rights, and damage to a developer’s reputation. Code Whisperer’s reference tracking continuously monitors suggested code for similarities with known open-source code, allowing developers to make informed decisions about incorporating it into their project and ensuring proper attribution.

"Image is a gif of a code editor window displaying a counting sort function, with a section of the code highlighted. The highlighted section is the implementation of counting sort by digit, suggested by CodeWhisperer. The gif includes a caption mentioning that the implementation is being referenced from MIT. This showcases the capability of CodeWhisperer's reference tracking."

Shift left application security

CodeWhisperer can scan code for hard-to-find vulnerabilities such as those in the top ten Open Web Application Security Project (OWASP), or those that don’t meet crypto library best practices, AWS internal security best practices, and others. As of this writing, CodeWhisperer supports security scanning in Python, Java, and JavaScript languages. Below is an illustration of identifying the most known CWEs (Common Weakness Enumeration) along with the ability to dive deep into the problematic line of code with a click of a button.

"Image is a gif of a code editor window displaying a code to download a file, with a section of the code highlighted. Below the code, there is an illustration of the identification of the most common Common Weakness Enumerations (CWEs) found in the code. However, it is mentioned that not all CWEs have been identified. Additionally, the illustration showcases the feature of being able to dive deep into the problematic line of code by clicking a button."

In the following example, CodeWhisperer provides file-by-file analysis of CWE’s and highlights the top 10 OWASP CWEs such as Unsensitized input is run as code, Cross-site scripting, Resource leak, Hardcoded credentials, SQL injection, OS command injection and Insecure hashing.

Image displays a screen with a proceeding from CodeWhisperer. The text highlights the file-by-file analysis of Common Weakness Enumerations (CWEs) and emphasizes the top 10 OWASP CWEs. These include CWE-94, CWE-95, and CWE-96, which pertain to the unsanitized input being executed as code. Additionally, CWE-20, CWE-79, and CWE-80 are related to cross-site scripting. Furthermore, CWE-400 and CWE-664 are associated with resource leaks, while CWE-798 relates to hardcoded credentials. CWE-89 refers to SQL injection, and CWE-77, CWE-78, and CWE-88 are connected to OS command injection. Lastly, CWE-327 and CWE-328 relate to insecure hashing.

Generating Test Cases

A good developer always writes tests. CodeWhisperer can help suggest test cases and verify the code’s functionality. CodeWhisperer considers boundary values, edge cases, and other potential issues that may need to be tested. In the example below, a comment referring to using fact_demo() function leads CodeWhisperer to suggest a unit test for fact_demo() while leveraging contextual details.

"Image is a gif displaying a code editor window, with a section of code highlighted. A comment within the code refers to the use of the fact_demo() function. CodeWhisperer is seen suggesting code for unit testing, leveraging contextual details related to the fact_demo() function. The background is a blurred office environment."

Also, CodeWhisperer can simplify creating repetitive code for unit testing. For example, if you need to create sample data using INSERT statements, CodeWhisperer can generate the necessary inserts based on a pattern.

"Image is a gif of a person typing on a computer keyboard, with an integrated development environment (IDE) on the screen. The person is entering a prompt to insert sample users into a table, with details such as username, password, and status. As the person types, CodeWhisperer builds out the insert query for the user. The IDE updates with the output query displayed below, which can be executed to insert the sample users into the table."

CodeWhisperer with Amazon SageMaker Studio and Jupyter Lab

CodeWhisperer works with SageMaker Studio and Jupyter Lab, providing code completion support for Python in code cells. To utilize CodeWhisperer, follow the setup instructions to activate it in Amazon SageMaker Studio and Jupyter Lab. To begin coding, see User actions.
The following illustration showcases CodeWhisperer’s code recommendations in SageMaker Studio. It demonstrates the suggested code based on comments for loading and analyzing a dataset.

"Image is a gif of an illustration showcasing CodeWhisperer's code recommendations in SageMaker Studio. The illustration shows a code editor window with a section of code highlighted. The code pertains to loading and analyzing a dataset. CodeWhisperer is seen providing code recommendations based on comments within the code. The recommendations appear in the form of a pop-up, with suggested changes displayed."

Conclusion

In conclusion, this blog has highlighted the numerous ways in which developers can leverage CodeWhisperer to increase productivity, streamline workflows, and ensure the development of secure code. By adopting Code Whisperer’s AI-powered features, developers can experience enhanced productivity, accelerated learning, and significant time savings.

To take advantage of CodeWhisperer and optimize your coding process, here are the next steps:

1. Visit feature page to learn more about the benefits of CodeWhisperer.

2. Sign up and start using CodeWhisperer.

3. Read about CodeWhisperer success stories

About the Authors

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Vamsi Cherukuri

Vamsi Cherukuri is a Senior Technical Account Manager at Amazon Web Services (AWS), leveraging over 15 years of developer experience in Analytics, application modernization, and data platforms. With a passion for technology, Vamsi takes joy in helping customers achieve accelerated business outcomes through their cloud transformation journey. In his free time, he finds peace in the pursuits of running and biking, frequently immersing himself in the thrilling realm of marathons.

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Dhaval Shah

Dhaval Shah is a Senior Solutions Architect at AWS, specializing in Machine Learning. With a strong focus on digital native businesses, he empowers customers to leverage AWS and drive their business growth. As an ML enthusiast, Dhaval is driven by his passion for creating impactful solutions that bring positive change. In his leisure time, he indulges in his love for travel and cherishes quality moments with his family.

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Nikhil Sharma

Nikhil Sharma is a Solutions Architecture Leader at Amazon Web Services (AWS) where he and his team of Solutions Architects help AWS customers solve critical business challenges using AWS cloud technologies and services.