AWS DevOps & Developer Productivity Blog
Category: Generative AI
Amazon Q Developer plugins now generally available for the AWS Management Console
Today, Amazon Web Services (AWS) announced the launch and general availability of Amazon Q Developer plugins for Datadog and Wiz in the AWS Management Console. When chatting with Amazon Q in the console, customers can access a subset of information from Datadog and Wiz services using natural language. Ask questions like @datadog do I have […]
Five ways to optimize code with Amazon Q Developer
Practical improvement and optimization of software quality requires expert-level knowledge across various subjects. As such, in this blog we shall look at how Amazon Q Developer can help improve your development team productivity and application stability by enabling automation around code optimization by improving your code’s quality, performance, application infrastructure specifications. The blog will also look […]
Code security scanning with Amazon Q Developer
A primary objective of software developers is to develop products that uphold the highest standards of data privacy and security, fostering trust and confidence among their users and customers. Developers seek to secure their software by identifying and mitigating security vulnerabilities in their codebase, thereby enhancing its resilience against cyber threats. Amazon Q Developer, a […]
How to identify inactive users of Amazon Q Developer
Generative AI is leading to many new features and capabilities. As a result, your employees may not know about all the new tools you are deploying. I was recently working with a customer that had deployed Amazon Q Developer for all their software developers. However, many developers didn’t know they had access to the productivity […]
Accelerate application upgrades with Amazon Q Developer agent for code transformation
In this blog, we will explore how Amazon Q Developer Agent for code transformation accelerates Java application upgrades. We will examine the benefits of this Generative AI-powered agent and outline strategies to achieve maximal acceleration, drawing from real-world success stories and best practices. Benefits of using Amazon Q Developer to upgrade your applications Amazon Q […]
Amazon Q Developer Code Challenge
Amazon Q Developer is a generative artificial intelligence (AI) powered conversational assistant that can help you understand, build, extend, and operate AWS applications. You can ask questions about AWS architecture, your AWS resources, best practices, documentation, support, and more. With Amazon Q Developer in your IDE, you can write a comment in natural language that […]
Accessing Amazon Q Developer using Microsoft Entra ID and VS Code to accelerate development
Overview In this blog post, I’ll explain how to use a Microsoft Entra ID and Visual Studio Code editor to access Amazon Q developer service and speed up your development. Additionally, I’ll explain how to minimize the time spent on repetitive tasks and quickly integrate users from external identity sources so they can immediately use […]
How A/B Testing and Multi-Model Hosting Accelerate Generative AI Feature Development in Amazon Q
Introduction In the rapidly evolving landscape of Generative AI, the ability to deploy and iterate on features quickly and reliably is paramount. We, the Amazon Q Developer service team, relied on several offline and online testing methods, such as evaluating models on datasets, to gauge improvements. Once positive results are observed, features were rolled out […]
Implementing Identity-Aware Sessions with Amazon Q Developer
“Be yourself; everyone else is already taken.” -Oscar Wilde In the real world as in the world of technology and authentication, the ability to understand who we are is important on many levels. In this blog post, we’ll look at how the ability to uniquely identify ourselves in the AWS console can lead to a […]
Testing your applications with Amazon Q Developer
Testing code is a fundamental step in the field of software development. It ensures that applications are reliable, meet quality standards, and work as intended. Automated software tests help to detect issues and defects early, reducing impact to end-user experience and business. In addition, tests provide documentation and prevent regression as code changes over time. […]









