Requirements Elaboration

Requirements Elaboration: Decision Discovery Skill

You help transform high-level desires into concrete, justified decisions by systematically exploring the decision space, identifying dependencies, and challenging default assumptions that would otherwise create mediocre implementations.

Core Principle

Every high-level requirement explodes into a decision tree. Every default hides assumptions that may be wrong for your specific context. The path to exceptional software is systematically questioning assumptions rather than accepting statistical averages.

Research from requirements engineering shows that 60-80% of project failures trace back to requirements problems, not technical execution.

The Decision Cascade Problem

Requirement Explosion Example

High-level: "Users should authenticate"

Immediate questions:

• Who are the users?
• What are they authenticating to?
• Why do they need authentication?
• When does authentication occur?
• Where does identity come from?
• How long do sessions last?

Second-order decisions:

• Identity source (internal database, LDAP, OAuth, SAML, multiple?)
• Authentication factors (password, MFA, biometric, hardware tokens?)
• Session management (duration, refresh, device binding, concurrent?)

Hidden assumptions defaulted without thought:

• "Users have email addresses"
• "Sessions should last 24 hours"
• "Passwords are acceptable"
• "Users have modern browsers"
• "Network is always available"

The Five Whys for Features

Systematic Questioning Process

Initial request: "We need user profiles"

Why 1: "Why do we need user profiles?"

• Answer: "To personalize the experience"
• Follow-up: "What personalization is needed?"

Why 2: "Why personalize the experience?"

• Answer: "Users want to see their own data"
• Follow-up: "What data is 'theirs'?"

Why 3: "Why separate data by user?"

• Answer: "Different departments use it differently"
• Follow-up: "How many departments? How different?"

Why 4: "Why not just department-level separation?"

• Answer: "Individual accountability needed"
• Follow-up: "For what specific actions?"

Why 5: "Why is individual accountability needed?"

• Answer: "Regulatory compliance for changes"
• Discovery: Real need is audit trail, not profiles

Actual requirement discovered:

• NOT needed: User profiles, personalization, preferences, avatars
• ACTUALLY needed: Action attribution, audit logging, department separation, read-only vs edit roles

Context Discovery Framework

User Context

Questions to ask:

• Who exactly will use this?
• How many concurrent users?
• What's their technical sophistication?
• What devices do they use?
• Where are they located?
• When do they use it?

Anti-patterns to catch:

• "Users" without qualification
• "Modern browsers" assumption
• "Mobile-friendly" without mobile users
• "Global" when it's single-office

Business Context

Questions to ask:

• What problem does this solve?
• What's it replacing?
• What's the cost of not having it?
• How does success get measured?
• What's the failure impact?
• Who pays for it?

Revelations these produce:

• "Replace spreadsheet" → Import/export critical
• "Compliance need" → Audit over features
• "Cost center" → Minimal maintenance crucial
• "Revenue generator" → Performance critical

Technical Context

Questions to ask:

• What systems must it integrate with?
• What are the hard constraints?
• What technical debt exists?
• What skills does the team have?
• What's the maintenance budget?
• What's the deployment environment?

Discoveries these enable:

• "On-premise only" → No cloud services
• "Legacy database" → Schema constraints
• "Small team" → Avoid exotic tech
• "No DevOps" → Simple deployment crucial

Decision Dependency Mapping

Dependency Types

Hard constraints: If A then must B

• If internal_only → must use corporate_identity_provider
• If regulated_industry → must have audit_trail
• If multi_tenant → must have data_isolation

Mutual exclusions: If A then cannot B

• If internal_only → cannot have social_login
• If on_premise → cannot use cloud_only_service

Soft preferences: If A then prefer B

• If small_team → prefer simple_tooling
• If high_scale → prefer horizontal_scaling
• If cost_sensitive → prefer open_source

Lynchpin Decision Identification

Find decisions that unlock or constrain many others:

Example lynchpin: "Is this application internal only?"

If internal_only = true:

• Enables: Use internal SSO, skip GDPR compliance, simplified security
• Prevents: Social login, public registration, multi-tenant architecture

If internal_only = false:

• Enables: External user registration, social auth, public API
• Requires: GDPR compliance, rate limiting, DDoS protection

Assumption Challenge Protocols

Default Assumptions to Always Question

User assumptions:

Scale assumptions:

Technical assumptions:

Statistical Gravity Resistance

Symptoms of gravity:

• "Industry standard" without justification
• "Best practice" without context
• "Everyone uses X" without evaluation
• "Modern apps have Y" without need
• "X is popular" without fit analysis

Resistance techniques:

Devil's Advocate:

1. Assume the opposite
2. List benefits of NOT having it
3. Find successful counterexamples
4. Calculate real cost
5. Question the rhetoric

Scenario Testing:

1. Write concrete user stories
2. Walk through actual workflows
3. Count real interactions
4. Time actual tasks
5. Measure real frequency

Decision Brief Template

Decision Brief:
  header:
    decision_id: "DEC-YYYY-NNN"
    title: "[Decision Name]"
    impact: "[Foundational/Major/Minor]"
    reversibility: "[Easy/Moderate/Difficult]"

  context:
    requirement_trace: "[Original requirement]"
    discovered_context:
      - "[Context item 1]"
      - "[Context item 2]"
    existing_constraints:
      - "[Constraint 1]"
      - "[Constraint 2]"

  options:
    option_1:
      name: "[Option name]"
      description: "[What this means]"
      pros: ["[Pro 1]", "[Pro 2]"]
      cons: ["[Con 1]", "[Con 2]"]
      cost: "[Low/Moderate/High]"
      risk: "[Low/Medium/High]"
      effort: "[Estimate]"

    eliminated_options:
      "[Option name]":
        reason: "[Why eliminated]"

  dependencies:
    decisions_this_enables: ["[Decision 1]"]
    decisions_this_requires: ["[Decision 2]"]

  recommendation:
    choice: "[Recommended option]"
    rationale: ["[Reason 1]", "[Reason 2]"]
    conditions: ["[Condition 1]", "[Condition 2]"]

Specification Generation

From Decisions to Specifications

Functional requirements:

• Source: Validated user needs
• Format: User stories with acceptance criteria
• Traceability: Links to decision records

Non-functional requirements:

• Source: Context discoveries
• Format: Measurable constraints
• Categories: Performance, Security, Usability, Compatibility, Compliance

Technical specifications:

• Source: Decision briefs
• Format: Implementation directives
• Includes: Technology choices, architecture patterns, data models

Anti-requirements (crucial for preventing scope creep):

• Source: Eliminated options
• Format: What NOT to build
• Examples: "No user customization", "No mobile version", "No public access"

Validation Patterns

Spike Types

Technical spike (1-3 days):

• Purpose: Validate technical feasibility
• Deliverables: Working code, performance metrics, risk assessment

User spike (2-5 days):

• Purpose: Validate user acceptance
• Deliverables: Clickable mockup, user feedback, workflow validation

Integration spike (3-5 days):

• Purpose: Validate system integration
• Deliverables: API communication proof, data flow, error handling

Reversal Triggers

Immediate reversal needed when:

• Fundamental assumption proved false
• Critical dependency unavailable
• Cost exceeds budget by >50%
• Legal/compliance blocker

Considered reversal when:

• Significant complexity discovered
• Better alternative found
• Requirements substantially changed
• Team lacks skills

Anti-Patterns

1. Analysis Paralysis

Pattern: Spending months gathering requirements through endless stakeholder meetings without building anything. Why it fails: Requirements evolve when users see working software. Perfect understanding is impossible upfront; stakeholders often don't know what they want until they see what they don't want. Fix: Time-box exploration phases. Build prototypes early. Validate assumptions through spikes rather than meetings. Aim for "good enough to start" rather than "complete."

2. Assumption Avalanche

Pattern: Accepting defaults without documentation—"obvious" choices everywhere, copy-paste architecture from previous projects. Why it fails: Undocumented decisions become invisible constraints. When problems emerge later, no one remembers why choices were made or whether alternatives were considered. Fix: Document every significant decision with rationale. Challenge defaults explicitly. Create decision briefs even for "obvious" choices so the reasoning is captured.

3. Feature Creep During Discovery

Pattern: "While we're at it" additions during requirements gathering. Nice-to-have becomes must-have through scope bleed. Why it fails: Discovery expands indefinitely. The "complete" feature set grows faster than implementation capacity. Core value gets buried under accumulated wants. Fix: Maintain strict must/should/could separation. Create anti-requirements (what you will NOT build). Revisit scope when additions emerge—something must leave if something enters.

4. Solutioning in Requirements

Pattern: Requirements specify technology choices: "Use React" instead of "Users need fast page loads." How before what. Why it fails: Technology choices belong to implementation. Premature technology constraints eliminate potentially better solutions and encode assumptions that may be wrong. Fix: Write requirements as user outcomes and constraints. Keep technology decisions separate. Let implementation choose how to achieve the what.

5. Single Stakeholder Capture

Pattern: Treating one vocal stakeholder as the voice of all users. Requirements reflect their preferences without broader validation. Why it fails: Individual stakeholders have individual biases. The loudest voice rarely represents typical users. Edge cases become core requirements. Fix: Identify all user categories. Get input from multiple representatives. Validate requirements with actual usage data where available. Weight inputs by user volume.

Success Metrics

• Decision velocity: Decisions made per week (clear trending up early, stabilizing later)
• Reversal rate: Decisions reversed / total decisions (target: <10%)
• Assumption validation rate: Validated / total assumptions (target: >80%)
• Requirement stability: Requirements unchanged after sign-off (target: >90%)

Integration

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Outbound (this skill enables)

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