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NEEDS_CLARIFICATION.md

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TODOs Requiring Clarification

Overview

This document tracks TODO items that require further clarification before implementation can proceed. Each item includes the original TODO, questions that need answering, and potential approaches.

P1 - High Priority Items Needing Clarification

1. Natural Language Test Specification Support

Original TODO: Add natural language test specification support

Questions Requiring Clarification:

  • What format should natural language test specifications take? (e.g., Gherkin, plain English, structured format?)
  • Should this integrate with existing test frameworks or create a new DSL?
  • What level of complexity should be supported in natural language descriptions?
  • How should ambiguous language be handled?

Potential Approaches:

  1. Gherkin-style Given/When/Then format
  2. Free-form English with AI interpretation
  3. Structured templates with fill-in-the-blank approach

Stakeholders to Consult: Product Owner, QA Team

2. AI-Driven Test Assertion Generation

Original TODO: Implement AI-driven test assertion generation

Questions Requiring Clarification:

  • What types of assertions should be automatically generated?
  • How should the AI determine what to assert on?
  • Should assertions be suggested or automatically applied?
  • What confidence threshold should trigger manual review?

Potential Approaches:

  1. Generate assertions based on page state changes
  2. Use historical test data to predict common assertions
  3. AI analysis of user stories to derive assertions

Stakeholders to Consult: QA Lead, Development Team

3. Conversational Interaction with Agents

Original TODO: Add conversational interaction with agents

Questions Requiring Clarification:

  • What conversation patterns should be supported?
  • Should agents maintain conversation history/context?
  • How should multi-turn conversations be handled?
  • What commands or queries should agents respond to?

Potential Approaches:

  1. Command-based interaction (e.g., "explore login flow")
  2. Q&A style for test insights
  3. Interactive debugging sessions

Stakeholders to Consult: UX Designer, Developer Experience Team

P2 - Medium Priority Items Needing Clarification

4. AI-Powered Visual Regression Testing

Original TODO: Implement AI-powered visual regression testing

Questions Requiring Clarification:

  • What constitutes a "significant" visual change?
  • Should the AI learn from user feedback on false positives?
  • How should dynamic content be handled?
  • What baseline management strategy should be used?

Potential Approaches:

  1. Perceptual diff with AI-determined thresholds
  2. Semantic understanding of UI components
  3. Layout-aware comparison ignoring cosmetic changes

Stakeholders to Consult: Design Team, QA Team

5. Smart Test Coverage Recommendations

Original TODO: Create smart test coverage recommendations

Questions Requiring Clarification:

  • What metrics define "good" test coverage?
  • Should recommendations be based on code analysis, user flows, or both?
  • How should critical paths be identified?
  • What format should recommendations take?

Potential Approaches:

  1. Risk-based coverage analysis
  2. User journey mapping to test coverage
  3. Code complexity analysis for test prioritization

Stakeholders to Consult: Engineering Manager, Product Owner

6. Multi-Agent Collaborative Exploration

Original TODO: Enable multi-agent collaborative exploration

Questions Requiring Clarification:

  • How should agents coordinate their activities?
  • What communication protocol should agents use?
  • How to prevent duplicate work or conflicts?
  • Should there be a hierarchy or peer-to-peer collaboration?

Potential Approaches:

  1. Master-worker pattern with task distribution
  2. Peer-to-peer with shared state management
  3. Specialized agents for different exploration aspects

Stakeholders to Consult: Architecture Team, Performance Team

Technical Implementation Questions

General AI Integration

  1. Model Selection: Which AI models should be used for different tasks?
  2. Performance Budget: What are acceptable latency thresholds for AI operations?
  3. Fallback Strategy: How should the system behave when AI services are unavailable?
  4. Cost Management: What's the budget for AI API calls?

Integration Architecture

  1. State Management: How should AI agent state be persisted across sessions?
  2. Scaling Strategy: How to handle concurrent AI operations?
  3. Caching Policy: What AI responses should be cached and for how long?

Next Steps for Clarification

  1. Schedule Stakeholder Meetings: Set up sessions with identified stakeholders
  2. Create POCs: Build small prototypes for high-uncertainty items
  3. Document Decisions: Record clarifications in implementation-log.md
  4. Update TODOs: Move clarified items back to main TODO.md with specifications

Priority Matrix for Clarification

Item Impact Uncertainty Priority
Natural Language Test Specs High High 1
Multi-Agent Collaboration High High 2
AI Assertion Generation Medium High 3
Conversational Agents Medium Medium 4
Visual Regression Medium Medium 5
Coverage Recommendations Low Medium 6

Last Updated: 2025-07-25