Category: Artificial Intelligence

According to the Well-Architected DevOps Guidance, “A peer review process for code changes is a strategy for ensuring code quality and shared responsibility. To support separation of duties in a DevOps environment, every change should be reviewed and approved by at least one other person before merging.” Development teams often implement the peer review process […]

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 […]

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 […]

“All code will become legacy”. This saying, widely recognized amongst software developers, highlights the reality of their day-to-day activities. While writing new code is an integral part of a developer’s role, a significant portion of their time is dedicated to refactoring and maintaining existing codebases. Developers typically encounter numerous challenges when attempting to understand and […]

Today, Amazon Web Services (AWS) announced the release of workspace context awareness in Amazon Q Developer chat. By including @workspace in your prompt, Amazon Q Developer will automatically ingest and index all code files, configurations, and project structure, giving the chat comprehensive context across your entire application within the integrated development environment (IDE). Throughout the […]

When Amazon Web Services (AWS) launched Amazon Q Developer agent for code transformation as a preview last year to upgrade Java applications, we saw many organizations desire to significantly accelerate their Java upgrades. Previously, these upgrades were considered daunting, a time-consuming manual task requiring weeks if not months of effort and with Amazon Q Developer they […]

Accelerate your workflow using Amazon Q to provide in-depth analysis and step-by-step guidance. Troubleshoot and resolve common issues across AWS services like EC2, VPC, IAM, Lambda, and S3.

Amazon Q feature development enables teams using Amazon CodeCatalyst to scale with AI to assist developers in completing everyday software development tasks. Developers can now go from an idea in an issue to a fully tested, merge-ready, running application code in a Pull Request (PR) with natural language inputs in a few clicks. Developers can […]

Maximizing the value from Enterprise Software tools requires an understanding of who and how users interact with those tools. As we have worked with builders rolling out Amazon CodeWhisperer to their enterprises, identifying usage patterns has been critical. This blog post is a result of that work, builds on Introducing Amazon CodeWhisperer Dashboard blog and […]

A Case Study Presented by CodeWhisperer Customizations Amazon CodeWhisperer is an AI-powered coding assistant that is trained on a wide variety of data, including Amazon and open-source code. With the launch of CodeWhisperer Customizations, customers can create a customization resource. The customization is produced by augmenting CodeWhisperer using a customer’s private code repositories. This enables […]