AI Coding Assistantsin Al Ain

• Module 1: Introduction to AI Coding Assistants
  • Understand AI coding assistants and their role in modern software development.
  • Explore AI-assisted programming concepts, capabilities, and limitations.
  • Identify common use cases across the software development lifecycle.
• Module 2: Overview of Popular AI Coding Tools
  • Explore tools such as GitHub Copilot, Amazon Q Developer, and other AI-powered coding assistants.
  • Understand tool features, supported languages, and development environment compatibility.
  • Compare capabilities and identify suitable tools for different programming tasks.
• Module 3: Setting Up AI Coding Environments
  • Configure compatible IDEs and AI coding assistant extensions.
  • Understand account setup, authentication, and configuration options.
  • Explore privacy settings and responsible tool usage.
• Module 4: AI-Assisted Code Completion and Generation
  • Generate code snippets using natural-language instructions.
  • Understand inline suggestions and context-aware code completion.
  • Refine generated code to meet specific functional requirements.
• Module 5: Effective Instructions for Coding Assistants
  • Write clear instructions for code generation and modification.
  • Provide relevant context, constraints, examples, and expected outputs.
  • Improve results through iterative instruction refinement.
• Module 6: Working with Different Programming Languages
  • Explore AI-assisted development with Python, Java, JavaScript, and other supported languages.
  • Generate functions, classes, and reusable programming components.
  • Review language-specific syntax and implementation suggestions.
• Module 7: AI-Assisted Application Development
  • Develop application components with AI coding support.
  • Understand how to structure code into modules and reusable functions.
  • Integrate generated components into existing projects.
• Module 8: Code Explanation and Comprehension
  • Use AI tools to understand unfamiliar code and programming logic.
  • Generate explanations for functions, classes, and complex code segments.
  • Analyse dependencies and identify areas requiring further investigation.
• Module 9: Code Refactoring and Maintainability
  • Explore AI-assisted code restructuring and simplification.
  • Identify duplicated logic and opportunities for reusable components.
  • Review suggested changes for readability, maintainability, and compatibility.
• Module 10: Debugging and Error Analysis
  • Use AI coding assistants to interpret error messages and stack traces.
  • Investigate potential causes of syntax, runtime, and logic errors.
  • Validate proposed fixes through execution and testing.
• Module 11: AI-Assisted Software Testing
  • Generate unit tests and basic test scenarios.
  • Explore edge cases and potential failure conditions.
  • Review test coverage and validate generated test code.
• Module 12: Documentation and Technical Communication
  • Generate code comments, docstrings, and technical summaries.
  • Create initial documentation for functions, modules, and APIs.
  • Review AI-generated documentation for accuracy and completeness.
• Module 13: Working with Existing Codebases
  • Understand how project context influences AI-generated suggestions.
  • Explore assistance for navigating unfamiliar repositories.
  • Review modifications for compatibility with existing architecture and conventions.
• Module 14: Security, Privacy, and Responsible AI
  • Identify security risks in AI-generated code.
  • Understand confidential data handling and secure coding practices.
  • Review generated code for vulnerabilities, unsafe dependencies, and inappropriate data exposure.
• Module 15: Best Practices and Productivity Workflows
  • Incorporate AI coding assistants into daily development activities.
  • Establish review practices for accepting, modifying, or rejecting suggestions.
  • Balance AI assistance with independent reasoning and developer judgement.
• Module 16: Capstone Project and Practical Assessment
  • Build a small application or feature using an AI coding assistant.
  • Apply code generation, debugging, testing, and documentation techniques.
  • Evaluate the solution for correctness, readability, security, and maintainability.
  • Present project results and document the AI-assisted development process.

Course Delivery

• Instructor-led sessions covering AI coding assistant concepts and practical workflows.
• Demonstrations of AI-assisted programming, code review, debugging, and testing.
• Hands-on exercises using compatible development environments and AI coding tools.
• Practical assignments designed around common software development scenarios.
• Capstone project to demonstrate AI-assisted development skills through StepMerit training in Al Ain, United Arab Emirates.