Mastering the SDLC with Artificial Intelligence

Audience: Software developers, testers, architects, DevOps engineers, 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.)
• 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. It's ideal for professionals who are already part of software delivery teams (developers, testers, DevOps engineers, architects, product owners).

AI Quest is an immersive, one-day workshop that empowers software teams to integrate cutting-edge AI tools throughout the Software Development Lifecycle (SDLC). Structured as an interactive game with progressive “levels,” this experience blends technical skill-building, creativity, and collaboration.

Participants will tackle real-world challenges across all stages of the SDLC — from requirement gathering and design to coding, testing, deployment, and monitoring — using AI-powered tools like ChatGPT, GitHub Copilot, and ML-based log analysis systems.

Designed for modern development environments, this workshop fosters AI fluency, team synergy, and a deep understanding of how to augment software delivery pipelines with generative AI and machine learning capabilities.

 Workshop Format

• Duration: 1 full day
• Style: Hands-on, gamified, highly interactive
• Structure: 6 progressive levels + final project challenge
• Tools Used: ChatGPT, GitHub Copilot, Mermaid, GitHub Actions, log analyzers, diagramming tools, and more
• Team-based: Participants work in small squads to solve each “level” collaboratively

⚔️ Level 1: The Discovery Dungeon – Secrets of Requirements

Mission: Use LLMs (ChatGPT) to extract structured requirements from vague input.
Key Activities:

• Interpret ambiguous product ideas or feature requests
• Use AI to:
  • Generate user stories and acceptance criteria
  • Suggest personas and scenarios
  • Generate visual artifacts (e.g., simple diagrams with Mermaid or draw.io)
    Outcome: Structured backlog of user stories + initial domain model/visuals

🔥 Level 2: The Design Forge – Architect’s Scroll

Mission: Use AI to create and validate architecture plans.
Key Activities:

• Use AI to:
  • Propose architectural style (monolith, microservices, serverless)
  • Generate high-level component and interaction diagrams
  • Scaffold class/module structures
• Challenge each other's choices through peer design reviews
  Outcome: Validated architecture + code skeleton

🧙‍♂️ Level 3: The Code Arena – Codex Gauntlet

Mission: Use AI copilots to implement features and improve code.
Key Activities:

• Use GitHub Copilot or ChatGPT to implement functionality
• 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 find bugs in others’ code.
Key Activities:

• 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 + bug fixes

⚙️ Level 5: The Pipeline Portals – Automaton Gate

Mission: Set up smart CI/CD pipelines with AI assistance.
Key Activities:

• Use AI to:
  • Define workflows (e.g., GitHub Actions)
  • Automate build, test, and deploy steps
  • Suggest anomaly detection/rollback policies
    Outcome: AI-assisted, working CI/CD pipeline script or flow

🏰 Level 6: The Monitoring Citadel – Watchtower of Logs

Mission: Analyze logs and use ML to detect anomalies and simulate recovery.
Key Activities:

• Analyze pre-populated or generated logs
• 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 everything learned to build a working SDLC loop for a mini-project.
Key Activities:

• Select a team mini-project (e.g., bug tracker, chatbot, microservice)
• Apply AI at each SDLC phase:
  • Requirements, Design, Code, Test, Deploy, Monitor
• Present outcomes in a short team demo

Peer voting or judging for most effective AI-powered pipeline
Outcome: End-to-end AI-enhanced SDLC implementation + 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
• 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