Generative AI for Software Developers
This course is designed for software developers, engineers, and architects who want to leverage Generative AI for code generation, debugging, optimization, and automation.
- Course fee
- Contact us
- Duration
- 5 days
- Group rate
- Ask an advisor
Course overview
This course is designed for software developers, engineers, and architects who want to leverage Generative AI for code generation, debugging, optimization, and automation.
Participants will explore:
AI-assisted coding with tools like GitHub Copilot, Chat-GPT, and Code Llama.
Using Generative AI for code refactoring, debugging, and testing.
Automating documentation, API development, and software design.
Enhancing DevOps workflows with AI-powered automation.
Understanding the ethical implications of AI in software development
By the end of the course, participants will be able to integrate Generative AI into their development workflow, improving productivity, code quality, and automation.
Training methodology
This course follows a hands-on, real-world approach, including:
Instructor-led sessions with live coding demonstrations
Practical coding exercises using AI-assisted tools
Case studies on AI-powered development workflows
Scenario-based problem-solving & debugging workshops
Capstone project: AI-powered development workflow implementation
Target competencies
- By the end of the course, participants will:
- Use Generative AI for faster, more efficient software development
- Enhance code quality through AI-driven suggestions and optimizations
- Automate repetitive development tasks (code generation, testing, documentation, etc.)
- Improve debugging and software maintenance using AI insights
- Leverage AI-powered DevOps tools for CI/CD and automation
Course objectives
Upon completing the course, participants will:
- Develop AI-assisted code using modern AI-powered tools
- Use AI for debugging, code refactoring, and optimization
- Enhance software documentation and test automation with AI
- Understand the ethical considerations of AI-generated code
- Integrate AI into DevOps workflows for enhanced productivity
Course outline
- Overview of Generative AI and its impact on coding
- Understanding AI models like GPT, LLa-MA, and Code-Gen for software development
- Exploring AI-powered development tools (GitHub Copilot, Chat-GPT, Tab-nine, Code Llama, etc.)
- Case studies of AI-assisted software development
- Generating code snippets, functions, and complete programs using AI
- AI-assisted code refactoring and optimization for performance
- Best practices for reviewing and validating AI-generated code
- Hands-on: Using AI tools to generate optimized code
- AI-assisted error detection and automatic debugging
- Using AI to analyze logs, suggest fixes, and optimize performance
- Generative AI for static code analysis and security vulnerability detection
- Hands-on: Using AI for debugging and troubleshooting
- AI-driven test case generation for unit and integration testing
- Automating test execution and reporting with AI-powered tools
- AI-powered flakiness detection and test maintenance strategies
- Hands-on: Generating and running AI-assisted test cases
- AI-generated API design, specification, and testing
- Automating API documentation and code explanations using AI
- Enhancing API security and optimization with AI-assisted monitoring
- Hands-on: Building and documenting APIs with AI tools
- Automating code reviews and CI/CD pipelines with AI
- AI-enhanced deployment strategies and infrastructure automation
- AI-driven log analysis, monitoring, and anomaly detection
- Hands-on: Implementing AI-based automation in DevOps workflows
- AI-powered recommendations for scalable software architectures
- AI-driven code organization and modularization
- Using AI to identify and implement design patterns effectively
- Hands-on: Optimizing software architecture using AI suggestions
- Using AI for legacy code understanding and documentation
- AI-powered code translation and modernization
- Automating bug fixes and dependency management with AI
- Hands-on: AI-assisted legacy code migration projects
- Ethical considerations in AI-assisted coding
- Security risks of AI-generated code and mitigation strategies
- AI model limitations: Ensuring correctness and avoiding bias
- Hands-on: Reviewing AI-generated code for security & ethics compliance
- Final project: Implementing an AI-assisted software development workflow
- Peer review and expert feedback on AI integration strategies
- Course recap & final Q&A session
- Certification and next steps for AI-driven development
Morning and evening classes are available. CompuBase Training Center, Abu Dhabi.
