Course Outline
Level 1: The Discovery Dungeon – Secrets of Requirements
Mission: Utilize LLMs (ChatGPT) to extract structured requirements from ambiguous input.
Key Activities:
- Interpret unclear product ideas or feature requests
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Use AI to:
- Generate user stories and acceptance criteria
- Suggest personas and scenarios
- Create visual artifacts (e.g., simple diagrams with Mermaid or draw.io)
- Outcome: A structured backlog of user stories and initial domain model/visuals
Level 2: The Design Forge – Architect’s Scroll
Mission: Use AI to create and validate architecture plans.
Key Activities:
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Use AI to:
- Propose architectural styles (monolith, microservices, serverless)
- Generate high-level component and interaction diagrams
- Scaffold class/module structures
- Conduct peer design reviews to challenge each other's choices
- Outcome: Validated architecture and code skeleton
Level 3: The Code Arena – Codex Gauntlet
Mission: Use AI copilots to implement features and enhance code quality.
Key Activities:
- Utilize GitHub Copilot or ChatGPT to implement functionality
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Refactor AI-generated code for:
- Performance
- Security
- Maintainability
- Inject “code smells” and run peer clean-up challenges
- Outcome: Functional, refactored, AI-generated codebase
Level 4: The Bug Swamp – Test the Darkness
Mission: Generate and improve tests with AI, then identify bugs in others’ code.
Key Activities:
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Use AI to generate:
- Unit tests
- Integration tests
- Edge case simulations
- Exchange buggy code with another team for AI-assisted debugging
- Outcome: Test suite, bug report, and bug fixes
Level 5: The Pipeline Portals – Automaton Gate
Mission: Set up intelligent CI/CD pipelines with AI assistance.
Key Activities:
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Use AI to:
- Define workflows (e.g., GitHub Actions)
- Automate build, test, and deploy steps
- Suggest anomaly detection/rollback policies
- Outcome: AI-assisted, functional CI/CD pipeline script or flow
Level 6: The Monitoring Citadel – Watchtower of Logs
Mission: Analyze logs and use machine learning to detect anomalies and simulate recovery.
Key Activities:
- Analyze pre-populated or generated logs
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Use AI to:
- Identify anomalies or error trends
- Suggest automated responses (e.g., self-healing scripts, alerts)
- Create dashboards or visual summaries
- Outcome: Monitoring plan or simulated intelligent alerting mechanism
Final Level: The Hero’s Arena – Build the Ultimate AI-Supported SDLC
Mission: Teams apply all learned concepts to build a working SDLC loop for a mini-project.
Key Activities:
- Select a team mini-project (e.g., bug tracker, chatbot, microservice)
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Apply AI at each SDLC phase:
- Requirements, Design, Code, Test, Deploy, Monitor
- Present outcomes in a short team demo
Peer voting or judging for the most effective AI-powered pipeline
Outcome: End-to-end AI-enhanced SDLC implementation and team showcase
By the end of this workshop, participants will be able to:
- Apply generative AI tools to extract and structure software requirements for government projects
- Generate architectural diagrams and validate design choices using AI
- Use AI copilots to implement and refactor production-grade code
- Automate test generation and perform AI-assisted debugging
- Design intelligent CI/CD pipelines that detect and react to anomalies
- Analyze logs with AI/ML tools to identify risks and simulate self-healing
- Demonstrate a fully AI-enhanced SDLC through a mini team project for government applications
Requirements
Audience: Software developers, testers, architects, DevOps engineers, and product owners
Participants should have:
- A working understanding of the Software Development Lifecycle (SDLC)
- Practical experience in at least one programming language (e.g., Python, Java, JavaScript, C#, etc.)
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Familiarity with:
- Writing and reading user stories or requirements
- Basic software design principles
- Version control (e.g., Git)
- Writing and executing unit tests
- Running or interpreting CI/CD pipelines
This is an intermediate-to-advanced workshop, designed for professionals who are already part of software delivery teams (developers, testers, DevOps engineers, architects, product owners). The content is tailored to enhance skills and practices that are essential for government projects.
Testimonials (1)
Lecturer's knowledge in advanced usage of copilot & Sufficient and efficient practical session
