AI-Powered Software Development equips developers with the knowledge and practical skills to use AI for code generation, debugging, testing, documentation, code optimization, and modern software engineering throughout the software development lifecycle.
Duration 5 Days – 35 hrs.
Overview
The AI-Powered Software Development Training Course is designed to help software professionals improve productivity, accelerate development cycles, and enhance software quality using Artificial Intelligence (AI) tools and modern development practices.
This course introduces participants to AI-assisted coding, intelligent debugging, automated testing, code generation, documentation automation, AI-driven DevOps, and the practical use of Generative AI tools within the Software Development Life Cycle (SDLC). Participants will learn how to integrate AI into daily development workflows while maintaining software quality, security, governance, and best practices.
The training combines lectures, demonstrations, hands-on exercises, workshops, and mini-project activities to provide practical experience using AI-powered development platforms and tools.
Objectives
- Understand the fundamentals of AI in software development
- Use AI-powered coding assistants effectively
- Apply AI tools for code generation and optimization
- Improve software quality through AI-assisted testing and debugging
- Automate documentation and development workflows using AI
- Integrate AI into Agile and DevOps environments
- Identify risks, limitations, and governance considerations when using AI
- Build simple AI-assisted applications and development workflows
- Increase productivity while maintaining coding standards and security
Target Audience
- Software Developers
- Full-Stack Developers
- Web Developers
- Mobile Application Developers
- QA Engineers and Testers
- DevOps Engineers
- Technical Leads
- System Analysts
- IT Project Managers
- Software Architects
- IT Professionals interested in AI-assisted development
Prerequisites
- Basic knowledge of programming concepts
- Experience in any programming language (e.g., JavaScript, Python, Java, C#, PHP)
- Basic understanding of software development lifecycle (SDLC)
- Familiarity with Git or version control is an advantage
- Basic understanding of APIs and web applications is recommended
Course Outline
Module 1: Introduction to AI-Powered Software Development
- Overview of Artificial Intelligence in software engineering
- Evolution of AI-assisted development
- Benefits and challenges of AI in development
- Understanding Generative AI and Large Language Models (LLMs)
- AI use cases across SDLC
- AI trends in modern software development
- Ethical and responsible AI usage
- AI limitations and risks
Hands-On Activities
- Exploring AI development tools
- Basic AI prompt exercises
Module 2: AI-Assisted Coding and Development
- Introduction to AI coding assistants
- AI-powered code completion and suggestions
- Code generation using AI prompts
- Writing effective prompts for developers
- AI-assisted refactoring techniques
- Improving code readability and maintainability
- AI for rapid prototyping
- Best practices when using AI-generated code
Hands-On Activities
- Generating code snippets
- Refactoring existing code using AI tools
- Creating APIs with AI assistance
Module 3: AI for Debugging and Software Testing
- AI-assisted debugging techniques
- Error analysis using AI tools
- Automated unit test generation
- AI-powered test case creation
- Intelligent bug detection
- AI in regression testing
- Code quality analysis
- Static and dynamic code analysis
Hands-On Activities
- Generating unit tests
- Debugging applications using AI
- AI-assisted issue resolution
Module 4: AI in DevOps and Automation
- AI integration in CI/CD pipelines
- AI-powered DevOps practices
- Infrastructure automation concepts
- AI for deployment monitoring
- AI-assisted log analysis
- Predictive monitoring and alerting
- AI in cloud-native environments
- Automating repetitive development tasks
Hands-On Activities
- AI-assisted pipeline generation
- Monitoring and troubleshooting exercises
Module 5: AI for Documentation and Productivity
- Automated technical documentation
- AI-generated API documentation
- AI for user stories and requirements gathering
- AI-assisted Agile documentation
- Knowledge management using AI
- AI for project estimation and planning
- Productivity improvement strategies
- Collaboration with AI tools
Hands-On Activities
- Creating technical documentation using AI
- Generating Agile artifacts
Module 6: Secure and Responsible AI Development
- Security risks of AI-generated code
- Secure coding practices with AI
- AI governance and compliance
- Data privacy and confidentiality
- Managing AI hallucinations and inaccuracies
- Intellectual property considerations
- Human review and validation processes
- AI adoption strategies for organizations
Hands-On Activities
- Reviewing AI-generated code for vulnerabilities
- AI governance workshop
Module 7: Building AI-Enhanced Applications
- Integrating AI APIs into applications
- Introduction to AI SDKs and frameworks
- Chatbot and assistant integration concepts
- AI-enhanced application architecture
- Prompt engineering fundamentals
- AI workflow orchestration
- Real-world AI development scenarios
- Future of AI-driven software engineering
Hands-On Activities
- Building a simple AI-powered application
- AI integration mini-project
Final Workshop and Capstone Activity
- End-to-end AI-powered development workflow
- Team-based practical exercises
- Mini capstone project presentation
- Best practices review
- Open forum and consultation
