What this book covers
Chapter 1, Introduction to Generative AI-Powered Assistants, walks you through what generative AI-powered assistants are and how they work. We will look at some of the types of assistants for software developers and application builders and how they help boost productivity and improve user experiences.
Chapter 2, Introducing and Setting Up Amazon Q Developer, lays down the fundamentals of Amazon Q Developer and its features. We will look at how to enable the service so that it can work with multiple IDEs, command line, and also with various AWS services.
Chapter 3, Understanding Auto-Code Generation Techniques, provides insights into different auto-code generation techniques that Amazon Q Developer can assist with.
Chapter 4, Boost Coding Efficiency for Python and Java with Auto-Code Generation, looks at how you can use Amazon Q Developer to suggest code in the two most prominent programming languages used by developers: Python and Java. We walk you through the different auto-code generation techniques by building an example application using both of these programming languages.
Chapter 5, Boost Coding Efficiency for C and C++ with Auto-Code Generation, looks at how you can use Amazon Q Developer to suggest code in C and C++.
Chapter 6, Boost Coding Efficiency for JavaScript and PHP with Auto-Code Generation, looks at how you can use Amazon Q Developer to suggest code in the two important programming languages used by web developers: JavaScript and PHP.
Chapter 7, Boost Coding Efficiency for SQL with Auto-Code Generation, looks at how you can use Amazon Q Developer to suggest code in the most widely used database management and data manipulation language: SQL.
Chapter 8, Boost Coding Efficiency for Command-Line and Shell Script with Auto-Code Generation, looks at how you can use Amazon Q Developer to suggest code in command line and shell scripts.
Chapter 9, Boost Coding Efficiency for JSON, YAML, and HCL with Auto-Code Generation, looks at how you can use Amazon Q Developer to suggest code in JSON, YAML, and HCL formats that are used in prominent infrastructure as code services such as AWS CloudFormation and Terraform.
Chapter 10, Customizing Code Recommendations, looks at how you can use Amazon Q Developer’s customization feature to allow code suggestions that align with the organization’s internal libraries, proprietary algorithmic techniques, and enterprise code style.
Chapter 11, Understanding Code References, looks at how to use Amazon Q Developer to indicate the references of the generated code. We will also look at how you can turn it on/off and opt out of references.
Chapter 12, Simplifying Code Explanation, Optimization, Transformation, and Feature Development, looks at how Amazon Q Developer helps to explain, refactor, fix, and optimize code. We will also look at how it can upgrade projects by transforming the code to a newer version of the programming language. The concept of feature development will also be discussed with the help of an example.
Chapter 13, Simplifying Scanning and Fixing Security Vulnerabilities in Code, provides insights into how Amazon Q Developer scans for code vulnerabilities and also suggests how to fix security issues in code.
Chapter 14, Accelerate Data Engineering on AWS, covers how Amazon Q Developer assists data engineers and developers with coding in many of the services and tools provided by AWS.
Chapter 15, Accelerate Building Solutions on AWS, looks at how you can use Amazon Q Developer to get AWS-specific guidance and recommendations on a variety of topics, such as solution architecture, best practices, optimizing resources, and cost. It also helps with troubleshooting errors and support.
Chapter 16, Accelerate the DevOps Process on AWS, looks at how you can use Amazon Q Developer inside Amazon CodeCatalyst to speed up the application code building process.
