Course Outline
Level 1: The Discovery Dungeon – Unveiling the Secrets of Requirements
Mission: Utilize LLMs (such as ChatGPT) to extract structured requirements from vague inputs.
Key Activities:
- Interpret ambiguous product ideas or feature requests
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Leverage AI to:
- Generate user stories and acceptance criteria
- Propose personas and scenarios
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Create visual artifacts (e.g., simple diagrams using Mermaid or draw.io)
Outcome: A structured backlog of user stories along with an initial domain model and visuals
Level 2: The Design Forge – The Architect’s Scroll
Mission: Employ AI to create and validate architectural 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 and module structures
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Engage in peer design reviews to critique and refine design choices
Outcome: Validated architecture plus a code skeleton
Level 3: The Code Arena – The Codex Gauntlet
Mission: Use AI copilots to implement features and enhance code quality.
Key Activities:
- Implement functionality using GitHub Copilot or ChatGPT
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Refactor AI-generated code to optimize for:
- Performance
- Security
- Maintainability
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Introduce 'code smells' and conduct peer clean-up challenges
Outcome: A functional, refactored codebase generated with AI assistance
Level 4: The Bug Swamp – Testing the Darkness
Mission: Generate and improve tests with AI, then identify bugs in other teams' code.
Key Activities:
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Use AI to generate:
- Unit tests
- Integration tests
- Edge case simulations
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Exchange buggy code with another team for AI-assisted debugging
Outcome: A comprehensive test suite, a detailed bug report, and applied bug fixes
Level 5: The Pipeline Portals – The Automaton Gate
Mission: Establish 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 deployment steps
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Propose anomaly detection and rollback policies
Outcome: An AI-assisted, working CI/CD pipeline script or workflow
Level 6: The Monitoring Citadel – The Watchtower of Logs
Mission: Analyze logs and utilize ML to detect anomalies and simulate recovery processes.
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)
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Create dashboards or visual summaries
Outcome: A monitoring plan or a simulated intelligent alerting mechanism
Final Level: The Hero’s Arena – Building the Ultimate AI-Supported SDLC
Mission: Teams apply all acquired knowledge to construct 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 across each SDLC phase:
- Requirements, Design, Code, Test, Deploy, and Monitor
- Present results in a concise team demo
Peer voting or judging for the most effective AI-powered pipeline
Outcome: An end-to-end AI-enhanced SDLC implementation and a team showcase
By the end of this workshop, participants will be able to:
- Apply generative AI tools to extract and structure software requirements
- Generate architectural diagrams and validate design choices using AI
- Utilize 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 respond 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
Requirements
Target Audience: Software developers, testers, architects, DevOps engineers, and product owners
Participants are expected to have:
- A functional understanding of the Software Development Lifecycle (SDLC)
- Practical experience with at least one programming language (e.g., Python, Java, JavaScript, C#, etc.)
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Familiarity with the following areas:
- Writing and interpreting user stories or requirements
- Fundamental software design principles
- Version control systems (e.g., Git)
- Writing and executing unit tests
- Running or interpreting CI/CD pipelines
This is an intermediate-to-advanced workshop. It is ideally suited for professionals who are already part of software delivery teams, including developers, testers, DevOps engineers, architects, and product owners.
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
