AWS Machine Learning Blog
How Accenture is using Amazon CodeWhisperer to improve developer productivity
Amazon CodeWhisperer is an AI coding companion that helps improve developer productivity by generating code recommendations based on their comments in natural language and code in the integrated development environment (IDE). CodeWhisperer accelerates completion of coding tasks by reducing context-switches between the IDE and documentation or developer forums. With real-time code recommendations from CodeWhisperer, you can stay focused in the IDE and finish your coding tasks faster.
In this post, we illustrate how Accenture uses CodeWhisperer in practice to improve developer productivity.
“Accenture is using Amazon CodeWhisperer to accelerate coding as part of our software
engineering best practices initiative in our Velocity platform,” says Balakrishnan
Viswanathan, Senior Manager, Tech Architecture at Accenture. “The Velocity team was
looking for ways to improve developer productivity. After searching for multiple options,
we came across Amazon CodeWhisperer to reduce our development efforts by up to 30%
and we are now focusing more on improving security, quality, and performance.”
Benefits of CodeWhisperer
The Accenture Velocity team has been using CodeWhisperer to accelerate their artificial intelligence (AI) and machine learning (ML) projects. The following summary highlights the benefits:
- The team is spending less time creating boilerplate and repetitive code patterns, and more time on what matters: building great software
- CodeWhisperer empowers developers to responsibly use AI to create syntactically correct and secure applications
- The team can generate entire functions and logical code blocks without having to search for and customize code snippets from the web
- They can accelerate onboarding for novice developers or developers working with an unfamiliar codebase
- They can detect security threats early in the development process by shifting the security scanning left to the developer’s IDE
In the following sections, we discuss some of the ways that the Accenture Velocity team has been using CodeWhisperer in more detail.
Onboarding developers on new projects
CodeWhisperer helps developers unfamiliar with AWS to ramp up faster on projects that use AWS services. New developers in Accenture were able to write code for AWS services such as Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB. In a short amount of time, they were able to be productive and contribute to the project. CodeWhisperer assisted developers by providing code blocks or line-by-line suggestions. It is also context-aware. Changing the instructions (comments) to be more specific results in CodeWhisperer generating more relevant code.
Writing boilerplate code
Developers were able to use CodeWhisperer to complete prerequisites. They were able to create a preprocessing data class just by typing “class to create preprocessing script for ML data.” Writing the preprocessing script took only a couple of minutes, and CodeWhisperer was able to generate entire code blocks.
Helping developers code in unfamiliar languages
A Java user new to the team was able to easily start writing Python code with the help of CodeWhisperer without worrying about the syntax.
Detecting security vulnerabilities in the code
Developers were able to detect security issues by choosing Run security scan in their IDE. Detailed insights on the security issues found are provided directly in the IDE. This helps developers detect and fix issues early.
“As a developer, using CodeWhisperer enables you to write code more quickly,” says Nino Leenus, AI Engineering Consultant at Accenture. “In addition, CodeWhisperer will help you code more accurately by eliminating typos and other typical errors with the aid of artificial intelligence. For a developer, writing the same code multiple times is tedious. By recommending the subsequent code pieces that you may need, AI code completion technologies reduce such repetitious coding.”
This post introduces CodeWhisperer, an AI coding companion by Amazon. The tool uses ML models trained on large datasets to provide suggestions and autocompletion for code, as well as generate entire functions and classes based on natural language descriptions. This post also highlights some of the benefits seen by Accenture when using CodeWhisperer, such as increased productivity and the ability to reduce the time and effort required for common coding tasks. You can activate CodeWhisperer in your favorite IDE today. CodeWhisperer automatically generates suggestions based on your existing code and comments. Visit Amazon CodeWhisperer to get started.
About the Authors
Balakrishnan Viswanathan is an AI/ML Solution Architect at Accenture. Collaborating with AABG, he devises and executes cutting-edge cloud-based strategies to tackle various AI/ML related challenges. Bala’s interests lie in both cooking and Photoshop, which he is passionate about.
Shikhar Kwatra is an AI/ML specialist solutions architect at Amazon Web Services, working with a leading Global System Integrator. He has earned the title of one of the Youngest Indian Master Inventors with over 500 patents in the AI/ML and IoT domains. Shikhar aids in architecting, building, and maintaining cost-efficient, scalable cloud environments for the organization, and supports the GSI partner in building strategic industry solutions on AWS. Shikhar enjoys playing guitar, composing music, and practicing mindfulness in his spare time.
Ankur Desai is a Principal Product Manager within the AWS AI Services team.
Nino Leenus is an AI Consultant at Accenture. She is expertise on developing End-to-End Machine learning solutions and its deployment using cloud. She is curious about latest tools and technologies in ML-Ops field. She loves traveling and trekking.