扉页
版权信息
+书签
Contributors
About the author
About the reviewers
Preface
Who this book is for
What this book covers
To get the most out of this book
Conventions used
Get in touch
Share Your Thoughts
Download a free PDF copy of this book
Part 1 Generative AI-Powered Assistant
Chapter 1 Introduction to Generative AI-Powered Assistants
What is generative AI?
Common challenges faced by developers/builders
Generative AI-powered assistants for developers/builders
How developers/builders benefit from assistants
Types of assistants for developers/builders
Summary
References
Chapter 2 Introducing and Setting Up Amazon Q Developer
Amazon Q nomenclature
Amazon Q Developer basics
Amazon Q Developer features
Auto-code generation
Code customizations
Code transformations
Code explanation, optimization, and update
Code feature development
Reference tracking
Security scanning
Integration with AWS services
Amazon Q Developer tiers
Amazon Q Developer free tier
Amazon Q Developer Pro tier
Amazon Q Developer setup for third-party IDEs
VS Code
JetBrains
Visual Studio
Amazon Q Developer setup for command line
Amazon Q Developer setup for AWS coding environments
Amazon SageMaker Studio
Amazon EMR Studio
JupyterLab
AWS Glue Studio
AWS Lambda
Build on AWS with support from Amazon Q Developer
Amazon Q permissions
Conversational Q&A capability
Troubleshoot AWS console errors
Troubleshoot networking issues
Optimal selection of Amazon EC2 instances
Assist with AWS Support cases
Assist during DevOps process in Amazon CodeCatalyst
Summary
References
Part 2 Generate Code Recommendations
Chapter 3 Understanding Auto-Code Generation Techniques
What is a prompt?
Single-line prompts for auto-code generation
Multi-line prompts for auto-code generation
Chain-of-thought prompts for auto-code generation
Chat with code assistant for auto-code generation
Common building methods of auto-code generation
Single-line code completion
Full function generation
Block completion
Line-by-line recommendations
Cross-reference existing code
Generating sample data
Writing unit tests
Explaining and documenting code
Updating existing code
Feature development
Code transformation
Summary
References
Chapter 4 Boost Coding Efficiency for Python and Java with Auto-Code Generation
An overview of the use case for weather data analysis
Application requirements – weather data analysis
Prerequisites to access the OpenWeatherMap API
Python auto-code generation using Amazon Q Developer for weather data analysis
Solution blueprint for weather data analysis
Use-case summary
Java auto code generation using Amazon Q Developer for weather data analysis
Solution blueprint for weather data analysis
Use-case summary
Summary
References
Chapter 5 Boost Coding Efficiency for C and C++ with Auto-Code Generation
Overview of programming language categories and foundational languages
Foundational languages C and C++, and auto-code generation support
C auto-code generation using Amazon Q Developer
C language basics
End-to-end C language use case development
C++ auto-code generation using Amazon Q Developer
C++ language basics
End-to-end C++ language use case development
C and C++ code explainability and optimization using Amazon Q Developer
Summary
References
Chapter 6 Boost Coding Efficiency for JavaScript and PHP with Auto-Code Generation
Overview of web development programming languages
JS auto-code generation using Amazon Q Developer
JS language basics
End-to-end JS use case development
PHP auto-code generation using Amazon Q Developer
PHP language basics
End-to-end PHP language use case development
JS and PHP code explainability and optimization using Amazon Q Developer
Summary
References
Chapter 7 Boost Coding Efficiency for SQL with Auto-Code Generation
Overview of database management languages
SQL auto-code generation for DBAs using Amazon Q Developer
Amazon RDS for MySQL basics for DBAs
SQL generation for DBAs
SQL auto-code generation for DEs using Amazon Q Developer
Amazon RDS for MySQL basics for DEs
SQL generation for DEs
SQL code explainability and optimization using Amazon Q Developer
Summary
References
Chapter 8 Boost Coding Efficiency for Command-Line and Shell Script with Auto-Code Generation
Overview of command-line and shell script
Command-line auto-code generation for system administrators using Amazon Q Developer
Disk space management CLI auto-code generation
User management CLI auto-code generation
Process management CLI auto-code generation
Library management CLI auto-code generation
Shell script auto-code generation for programmers using Amazon Q Developer
Shell script basics
Shell script generation for programmers
Shell script explainability and optimization using Amazon Q Developer
Summary
References
Chapter 9 Boost Coding Efficiency for JSON, YAML, and HCL with Auto-Code Generation
Overview of IaC languages
JSON and YAML auto-code generation for AWS CloudFormation using Amazon Q Developer
AWS CloudFormation basics
JSON auto-code generation
YAML auto-code generation
HCL auto-code generation for Terraform using Amazon Q Developer
Terraform basics
HCL auto-code generation
JSON, YAML, and HCL code explainability and optimization using Amazon Q Developer
Summary
References
Part 3 Advanced Assistant Features
Chapter 10 Customizing Code Recommendations
Prerequisites for Amazon Q customizations
Creating a customization in Amazon Q
Connecting to a repository
Preparing customizations
Activating and deactivating customizations
Evaluating and optimizing the customization
Score evaluation
Optimizing the customization
Creating multiple versions of customization
Adding users and groups to the customization
Using customization in IDEs
Summary
References
Chapter 11 Understanding Code References
What are code references?
Enabling, disabling, and opting out of code references
Code reference example
Summary
References
Chapter 12 Simplifying Code Explanation, Optimization, Transformation, and Feature Development
Explaining and updating code
Explaining code
Refactoring code
Fixing code
Optimizing code
Sending code to prompt
Workspace context code awareness
Transforming code
Prerequisites for code transformation using Amazon Q
How code transformation using Amazon Q works
Code transformation example
Developing code features
Summary
References
Chapter 13 Simplifying Scanning and Fixing Security Vulnerabilities in Code
The importance of code-security scanning
Types of code scans using Amazon Q
Initiating security scans
Scanning while coding
Scanning the whole project
Addressing vulnerability findings
Viewing security findings in detail
Seeking an explanation of the issue
Summary
References
Part 4 Accelerate Build on AWS
Chapter 14 Accelerate Data Engineering on AWS
Code assistance options with AWS services
Code assistance integration with AWS Glue
Use case for AWS Glue
Solution blueprint
Data preparation
Solution – Amazon Q Developer with an AWS Glue Studio notebook
Solution – Amazon Q Developer with AWS Glue
Code assistance integration with Amazon EMR
Use case for Amazon EMR Studio
Solution – Amazon Q Developer with Amazon EMR Studio
Code assistance integration with AWS Lambda
Use case for AWS Lambda
Solution blueprint
Data preparation
Solution – Amazon Q Developer with AWS Lambda
Code assistance integration with Amazon SageMaker
Use case for Amazon SageMaker
Solution blueprint
Data preparation
Solution – Amazon Q with Amazon SageMaker Studio
Code assistance integration with Amazon Redshift
Use case for Amazon Redshift
Solution blueprint
Data preparation
Solution – Amazon Q with Amazon Redshift
Summary
References
Chapter 15 Accelerate Building Solutions on AWS
Key steps for building solutions on AWS
Use case for leveraging Amazon Q features during the build process on AWS
Conversational Q&A capability
Selecting the optimal Amazon EC2 instance
Network troubleshooting assistance
Chatting with AWS Support
Troubleshooting console errors with Amazon Q
Troubleshooting Lambda errors with Amazon Q
Awareness of AWS account resources
Analyzing resources with Amazon Q
Analyzing costs with Amazon Q
Summary
References
Chapter 16 Accelerate the DevOps Process on AWS
Challenges during the DevOps process
Introduction to Amazon CodeCatalyst
Exploring Amazon Q's capabilities in Amazon CodeCatalyst
Amazon Q's feature development capability in Amazon CodeCatalyst
Use case for leveraging Amazon Q feature development in Amazon CodeCatalyst
Assigning issues to Amazon Q
Generating an approach and providing feedback
Generating pull requests
Reviewing and revising code changes
Merging code changes in the repository
Amazon Q's summarizing capability in Amazon CodeCatalyst
Pull request comment summary
Pull request description
Summary
Final thoughts
References
Index
Why subscribe?
Other Books You May Enjoy
Packt is searching for authors like you
Share Your Thoughts
Download a free PDF copy of this book

简介

版权

Generative AI-Powered Assistant for Developers