Generative AI-Powered Software Engineering with AI Coding Assistants

The field of software engineering is undergoing a rapid transformation due to the usage of large language models and generative AI frameworks. The days when developers had to remember every syntax detail or repeatedly implement common design patterns from scratch have gone behind them. AI coding assistants like GitHub Copilot and Amazon CodeWhisperer are reshaping how developers can write code, enabling them to focus more on problem-solving and less on code boilerplate implementation. This course will equip the learners with the skills to harness these AI-powered tools, turning them from simple code completion utilities into powerful development partners. 

Contents

1. Understanding AI Coding Assistants
2. Building Coding Solutions with AI Assistants
3. GitHub Copilot
4. Amazon CodeWhisperer
5. Prompt Engineering through AI Assistants
6. Code Generation
7. Code Debugging
8. Code Integration and Testing
9. AI-Assisted Code Review and Quality Techniques
10. Code Review Prompting Techniques and Patterns
11. Documentation Generation and Code Analysis
12. Code Performance Optimization
13. Practical Use Cases on AI Coding Assistants for Software Development

Course Objectives

1. Analyse the Technological Landscape of AI Coding Assistants.
2. Create complex coding solutions by strategically leveraging AI-generated suggestions and recommendations.
3. Implement AI coding assistants such as GitHub Copilot and Amazon CodeWhisperer (Amazon Q) into software development processes for efficient code generation and problem-solving.
4. Assess code quality, performance, and vulnerabilities using AI-powered review tools.
5. Critique and refine code suggestions to maintain high software development standards.