CODITECT Estimation Engine - Project Requirements Document (PRD)

Document Version: 1.0 Created: 2025-11-22 Document Owner: Hal Casteel, Founder/CEO/CTO, AZ1.AI INC. Project Type: New CODITECT Submodule (ops category) Repository: coditect-ops-estimation-engine Status: Requirements Definition Phase

Executive Summary
Problem Statement
Strategic Context
User Personas
Functional Requirements
Technical Requirements
Use Cases & User Stories
Data Model
API Specification
Reporting Requirements
Integration Plan
Success Metrics
Risk Assessment
Implementation Roadmap
Budget & Resources

Executive Summary

Purpose

The CODITECT Estimation Engine is a production-ready estimation, budgeting, and reporting tool for software projects using evidence-based methodologies. It provides developers, project managers, and executives with accurate cost/time/resource estimates based on industry-standard calculation methods and historical project data.

Goals

Primary Goals:

Automate Software Estimation - Replace manual spreadsheets with automated calculation engine
Evidence-Based Methodology - Use COCOMO II, Function Points, Story Points, and bottom-up WBS
Historical Accuracy - Learn from past projects to improve future estimates
Multi-Format Reports - Generate budgets, timelines, Gantt charts, risk analysis
CODITECT Integration - Slash commands, agents, skills for seamless workflow

Secondary Goals:

Codebase Analysis - Automatically count LOC, complexity, language distribution
GitHub/JIRA Integration - Import actual data for estimate validation
Sales Enablement - Generate customer quotes from requirements
Audit Trail - Track estimate vs. actual for accountability

Success Criteria

By Production Launch:

✅ Support all 6 estimation methodologies (COCOMO II, Bottom-Up, Story Points, Function Points, Three-Point, Parametric) ✅ Automated codebase analysis via cloc/tokei integration ✅ 5+ report formats (Budget, Timeline, Risk, Comparison, Export) ✅ CODITECT integration (/estimate command, estimation-calculator skill, budget-analyst agent) ✅ Historical project database with 10+ reference projects ✅ <2s estimation latency for typical projects ✅ Accuracy within ±20% for projects with historical data ✅ Complete API documentation (OpenAPI 3.1) ✅ Web UI + CLI + API access modes

Value Proposition

For Project Managers:

"Estimate project costs in 5 minutes instead of 5 hours"
"Evidence-based budgets validated with historical data"

For CTOs/CEOs:

"Validate vendor quotes and internal estimates with industry models"
"Track estimate accuracy over time to improve budgeting"

For Developers:

"Estimate feature work using story points + team velocity"
"Integrate estimation into development workflow"

For Sales Teams:

"Generate customer quotes from requirements in minutes"
"Provide confidence intervals and risk analysis"

Problem Statement

What Problem Does This Solve?

Current Pain Points:

Manual Estimation is Slow - Teams spend hours in spreadsheets calculating budgets
Inconsistent Methodologies - Different people use different estimation approaches
No Historical Learning - Past projects don't inform future estimates
Optimism Bias - Estimates are often 50-100% under actual costs
No Validation - No way to verify if vendor quotes are reasonable
Disconnected Tools - Estimation happens outside development workflow

Impact of Poor Estimation:

Projects run over budget by 50-100% (average software project)
Timeline delays damage customer relationships
Inaccurate quotes lead to unprofitable contracts
Lack of accountability for estimation quality
Difficult to justify budget requests to stakeholders

Who Has This Problem?

Primary Users:

Project Managers - Creating project budgets before development
CTOs/VPs Engineering - Validating budget proposals and resource allocation
Founders/CEOs - Approving development investments
Sales Engineers - Generating customer quotes from requirements

Secondary Users:

Developers - Estimating story/task effort
Product Managers - Prioritizing features by cost
Consultants - Providing clients with accurate SOWs
Auditors - Verifying project cost accuracy

Why Does This Matter?

Business Context:

Software projects are notorious for cost overruns (Standish Group: 45% over budget)
Estimation errors reduce profitability and damage reputation
Executives need data-driven budgets for investment decisions
Sales teams need accurate quotes to close deals profitably
Historical tracking enables continuous improvement

AZ1.AI/CODITECT Context:

CODITECT customers are building software projects
They need to estimate their own development costs
Integration with CODITECT workflow provides unique value
Differentiates CODITECT from generic development tools
Enables CODITECT to provide end-to-end project lifecycle support

Strategic Context

Ecosystem Role

CODITECT Architecture Position:

coditect-rollout-master/
├── submodules/
│   ├── ops/                              # Operations category
│   │   ├── coditect-ops-estimation-engine/  # ⭐ THIS SUBMODULE
│   │   ├── coditect-ops-license/         # License management
│   │   ├── coditect-ops-distribution/    # Installation/updates
│   │   ├── coditect-ops-compliance/      # Compliance tracking
│   │   └── coditect-ops-projects/        # Project templates

Category: ops (Operations) Priority: P1 (Important - Revenue enabler) Dependencies:

coditect-core - Agent framework, commands, skills
coditect-ops-projects - Historical project data
coditect-cloud-backend - Optional cloud storage for estimates

Dependents:

Sales teams (quote generation)
Project planning workflows
Budget approval processes
Customer project templates

Integration with CODITECT Platform

Distributed Intelligence Pattern:

coditect-ops-estimation-engine/
├── .coditect -> ../../../.coditect    # Symlink to master brain
├── .claude -> .coditect               # Claude Code compatibility
├── src/                               # Estimation engine code
├── data/                              # Historical project database
├── reports/                           # Generated reports
└── scripts/                           # CLI tools

CODITECT Components Added:

Agent: budget-analyst - Specialized agent for financial analysis and estimation
Command: /estimate - Quick estimation slash command
Skill: estimation-calculator - Reusable estimation patterns
Script: estimate-project.py - CLI estimation tool
Script: analyze-codebase.py - Automated LOC/complexity analysis

Market Positioning

Competitive Landscape:

Tool	Strengths	Weaknesses
Jira Roadmaps	Team velocity tracking	No COCOMO/Function Points
Microsoft Project	Timeline/Gantt charts	No evidence-based estimation
COCOMO II Tool	Industry-standard model	Standalone, no integration
Story Point Calculators	Agile-friendly	No bottom-up or parametric
Spreadsheets	Flexible	Manual, error-prone, no learning

CODITECT Advantage:

✅ All 6 methodologies in one tool ✅ Integrated with development workflow ✅ Historical learning from project database ✅ AI-assisted estimation (budget-analyst agent) ✅ Automated codebase analysis ✅ Multi-format reporting ✅ API-first architecture

Unique Value Proposition:

"The only estimation engine that combines industry-standard methodologies with AI-assisted analysis, automated codebase scanning, and seamless integration into your CODITECT development workflow."

User Personas

Persona 1: Project Manager (Primary)

Name: Sarah Chen Role: Senior Project Manager at SaaS Startup Experience: 8 years managing software projects

Goals:

Create accurate project budgets before development starts
Track estimate vs. actual to improve accuracy over time
Justify budget requests to executives with data
Identify high-risk areas requiring contingency

Pain Points:

Spends 4-6 hours per project on manual estimation
Estimates often 50% under actual costs
No consistent methodology across projects
Difficult to explain variances to stakeholders

Workflows:

Receives project requirements from product team
Breaks down into work breakdown structure (WBS)
Estimates effort for each task (hours/days)
Applies overhead factors (testing, PM, rework)
Calculates total cost and timeline
Generates budget report for executive approval
Tracks actual vs. estimate during execution

Success Criteria:

Reduce estimation time from 6 hours to 1 hour
Improve accuracy from ±50% to ±20%
Generate professional budget reports automatically
Have historical data to defend estimates

Estimation Engine Usage:

Uses /estimate command in Claude Code
Inputs WBS tasks with granular estimates
Reviews AI-suggested overhead factors
Exports budget as PDF for executives
Updates with actuals for historical learning

Persona 2: CTO/VP Engineering (Primary)

Name: Marcus Rodriguez Role: CTO at Enterprise Software Company Experience: 15 years in engineering leadership

Goals:

Validate budget proposals from project managers
Make data-driven resource allocation decisions
Evaluate vendor quotes for reasonableness
Improve organizational estimation maturity

Pain Points:

No way to verify if estimates are realistic
Different teams use different methodologies
Vendor quotes vary wildly (2x-5x difference)
No historical data to benchmark against

Workflows:

Reviews budget proposals from PM team
Challenges assumptions and overhead factors
Compares against historical similar projects
Evaluates vendor proposals for outsourced work
Approves or requests revisions
Tracks estimation accuracy over time

Success Criteria:

Ability to validate estimates in <30 minutes
Access to historical project database for benchmarking
Confidence intervals and risk analysis for decisions
Year-over-year improvement in estimation accuracy

Estimation Engine Usage:

Reviews budget-analyst agent risk assessments
Compares parametric model against bottom-up estimates
Uses COCOMO II to validate vendor quotes
Generates comparison reports (estimate vs. actual)
Tracks team velocity and productivity metrics

Persona 3: Founder/CEO (Secondary)

Name: Emily Tran Role: CEO at Early-Stage Startup Experience: 3 years running company, non-technical background

Goals:

Understand development costs for investor presentations
Make build vs. buy decisions with accurate data
Set realistic timelines for product launches
Avoid budget surprises that threaten runway

Pain Points:

Can't evaluate if engineering team's estimates are reasonable
Vendor quotes seem expensive but hard to verify
No visibility into what drives software costs
Past projects have gone 2x over budget

Workflows:

Receives product requirements from product team
Asks CTO for development estimate
Evaluates estimate against budget/runway
Decides to approve, defer, or descope
Presents budget to board/investors
Monitors actual spending vs. budget

Success Criteria:

Understand cost drivers (LOC, complexity, team size)
Get confidence intervals (best/worst/likely scenarios)
Compare internal estimates against industry benchmarks
Explain budget to non-technical stakeholders

Estimation Engine Usage:

Reviews executive summary reports
Uses three-point estimation for scenario planning
Compares function point cost against vendor quotes
Shares Gantt charts with investors
Tracks burn rate against original estimate

Persona 4: Sales Engineer (Secondary)

Name: Jordan Kim Role: Pre-Sales Engineer at Software Consulting Firm Experience: 5 years in pre-sales and solution architecture

Goals:

Generate accurate customer quotes quickly
Win deals with competitive but profitable pricing
Provide scope-of-work (SOW) documents
Set realistic customer expectations

Pain Points:

Manual quote creation takes 2-3 days
No consistent pricing methodology
Quotes are often too low (unprofitable) or too high (lose deal)
Difficult to justify pricing to customers

Workflows:

Receives RFP or customer requirements
Schedules scoping call with customer
Documents functional requirements
Estimates development effort
Applies pricing model (hourly rate × hours)
Generates SOW with timeline and milestones
Presents to customer and negotiates

Success Criteria:

Generate quote in <2 hours instead of 2 days
Win rate >40% with profitable margins
Professional SOW documents with Gantt charts
Customers trust pricing as fair and transparent

Estimation Engine Usage:

Uses function point analysis for requirements
Applies company's standard hourly rates
Generates PDF quote with risk analysis
Exports timeline as Gantt chart
Tracks won/lost deals by estimate accuracy

Persona 5: Developer (Tertiary)

Name: Alex Patel Role: Senior Full-Stack Developer Experience: 6 years software development

Goals:

Estimate story/task effort for sprint planning
Understand team velocity for capacity planning
Improve personal estimation accuracy
Advocate for realistic timelines

Pain Points:

Story point estimation feels arbitrary
No historical data to reference for similar tasks
Pressure to underestimate to make commitments
Unclear how individual estimates roll up to project budget

Workflows:

Reviews user story in sprint planning
Estimates story points based on complexity
Discusses with team to reach consensus
Commits to sprint based on team velocity
Tracks actual hours during development
Updates estimate if scope changes

Success Criteria:

Story point estimates within ±1 point of actual
Personal velocity is predictable over time
Can reference similar past stories for calibration
Transparent connection between story points and budget

Estimation Engine Usage:

Uses story point calculator with historical velocity
Reviews similar completed stories for reference
Tracks personal velocity trends over time
Converts story points to hours for PM budgets
Gets automated complexity analysis for new tasks

Functional Requirements

FR-1: Estimation Methodologies

Must Support All 6 Industry-Standard Methods:

FR-1.1: COCOMO II (Constructive Cost Model)

Purpose: Industry-standard parametric model for software estimation

Inputs:

Size: KLOC (thousands of lines of code) OR Function Points (auto-converted)
Scale Factors (5): Precedentedness, Development Flexibility, Architecture/Risk Resolution, Team Cohesion, Process Maturity
Effort Multipliers (17): Product attributes (reliability, database size, complexity), Platform (execution time, storage), Personnel (analyst capability, programmer capability, experience), Project (tools, schedule, multisite)

Formula:

Effort (person-months) = A × Size^E × ∏(EM_i)
where:
  A = calibration constant (default 2.94)
  E = exponent (0.91 + 0.01 × Σ(SF_i))
  EM_i = effort multiplier values (0.75 - 2.00)

Outputs:

Effort (person-months)
Development time (months)
Team size (people)
Cost ($) = Effort × Blended Hourly Rate × Hours/Month

Acceptance Criteria:

Accurate COCOMO II calculation matching COPSEMO reference tool
Auto-convert Function Points to KLOC using language-specific ratios
All 5 scale factors configurable (0-5 scale)
All 17 effort multipliers configurable
Support for multiple language productivity rates
Generate sensitivity analysis (which factors impact most)

FR-1.2: Bottom-Up Estimation (Work Breakdown Structure)

Purpose: Granular task-by-task estimation with rollup to project total

Inputs:

WBS Hierarchy: Project → Phases → Modules → Tasks
Task Details: Name, description, estimated hours, assigned role
Roles: Junior Dev, Mid Dev, Senior Dev, Lead Dev, PM, QA, DevOps (with hourly rates)
Overhead Factors: Testing (%), PM (%), Infrastructure (%), Rework (%), Contingency (%)

Process:

User creates hierarchical WBS (tree structure)
For each leaf task, enters estimated hours
Assigns role(s) to each task
System calculates:
- Direct effort (sum of all task hours)
- Overhead (testing, PM, infra, rework)
- Contingency (risk buffer)
- Total effort (direct + overhead + contingency)
- Total cost (effort × blended rate)
- Duration (effort / team size / availability %)

Outputs:

Total effort (hours)
Total cost ($)
Duration (weeks/months)
Cost breakdown by role
Cost breakdown by phase/module
Critical path analysis

Acceptance Criteria:

Support unlimited WBS depth (nesting)
Drag-and-drop task reorganization
Bulk import from CSV/Excel
Auto-calculate overhead percentages
Visualize WBS as tree diagram
Export WBS to Microsoft Project format

FR-1.3: Story Points + Velocity

Purpose: Agile-friendly estimation using historical team velocity

Inputs:

Stories: List of user stories with story point estimates (1, 2, 3, 5, 8, 13, 21)
Team Velocity: Average story points completed per sprint (from historical data)
Sprint Duration: Length of sprint in weeks (default 2)
Team Size: Number of developers on team

Calculation:

Total Story Points = Σ(story_points)
Number of Sprints = Total Story Points / Team Velocity
Duration (weeks) = Number of Sprints × Sprint Duration
Effort (hours) = Total Story Points × Hours per Point × Team Size
Cost ($) = Effort × Blended Hourly Rate

Outputs:

Total story points
Number of sprints required
Duration (weeks)
Effort (hours)
Cost ($)
Velocity trend chart (if historical data available)

Acceptance Criteria:

Import stories from JIRA/Linear API
Calculate team velocity from last 3-6 sprints
Support multiple teams with different velocities
Fibonacci sequence story point picker
Planning poker consensus tracking
Burndown chart projection

FR-1.4: Function Point Analysis

Purpose: Language-agnostic size measurement based on functionality

Inputs:

External Inputs (EI): User inputs, forms (Simple/Average/Complex)
External Outputs (EO): Reports, screens (Simple/Average/Complex)
External Inquiries (EQ): Queries, lookups (Simple/Average/Complex)
Internal Logical Files (ILF): Database tables (Simple/Average/Complex)
External Interface Files (EIF): External data sources (Simple/Average/Complex)
Complexity Adjustment Factors (14): Data communications, distributed functions, performance, etc.

Calculation:

Unadjusted Function Points (UFP) = Σ(count × weight)
  where weight based on complexity (Simple/Avg/Complex)

Value Adjustment Factor (VAF) = 0.65 + (0.01 × Σ(CAF_i))
  where CAF_i = 0-5 rating for 14 factors

Adjusted Function Points (AFP) = UFP × VAF

Effort (hours) = AFP × Language Productivity Factor
  (e.g., Python: 20 hours/FP, Java: 30 hours/FP)

Outputs:

Unadjusted Function Points
Adjusted Function Points
Estimated KLOC (converted)
Effort (hours)
Cost ($)

Acceptance Criteria:

Guided wizard for function point counting
Language-specific productivity tables (15+ languages)
Convert Function Points to KLOC and vice versa
Export as IFPUG-compliant report
Historical FP/KLOC ratio tracking by language

FR-1.5: Three-Point Estimation (PERT)

Purpose: Probabilistic estimation with optimistic, likely, pessimistic scenarios

Inputs:

Optimistic (O): Best-case effort (10th percentile)
Most Likely (M): Realistic effort (50th percentile)
Pessimistic (P): Worst-case effort (90th percentile)
Confidence Level: Desired confidence (e.g., 80%, 90%)

Formulas:

Expected Effort (E) = (O + 4M + P) / 6
Standard Deviation (σ) = (P - O) / 6
Variance = σ²

Confidence Intervals:
  68% confidence: E ± σ
  95% confidence: E ± 2σ
  99.7% confidence: E ± 3σ

Outputs:

Expected effort (weighted average)
Standard deviation (uncertainty measure)
Confidence intervals (range of outcomes)
Probability distribution graph
Risk level (High if P > 2M)

Acceptance Criteria:

Support task-level, module-level, and project-level PERT
Monte Carlo simulation for combined uncertainty
Visualize probability distribution as bell curve
Identify high-variance tasks for risk mitigation
Export scenarios as separate estimates

FR-1.6: Parametric Models (Historical Regression)

Purpose: Statistical prediction based on similar past projects

Inputs:

Project Attributes: Size (KLOC, FP), Domain, Team Size, Language, Platform
Historical Database: Past projects with actual effort/cost/duration
Similarity Filters: Domain, language, team size range

Process:

Filter historical projects by similarity criteria
Build regression model: Effort = f(Size, Team, Complexity, ...)
Apply model to new project attributes
Calculate confidence interval based on model fit (R²)

Outputs:

Predicted effort (hours)
Predicted cost ($)
Predicted duration (weeks)
Confidence interval (e.g., ±20%)
Similar projects used in model
Model accuracy (R² value)

Acceptance Criteria:

Minimum 10 historical projects required for model
Support linear, polynomial, and exponential regression
Filter by domain, language, team size, year
Show which projects contributed to prediction
Warn if new project is outside training data range
Auto-update model as new projects complete

FR-2: Automated Codebase Analysis

Purpose: Automatically measure project size without manual counting

FR-2.1: Lines of Code (LOC) Counting

Tools:

Primary: cloc (Count Lines of Code) - https://github.com/AlDanial/cloc
Secondary: tokei (for Rust-friendly alternative)

Metrics:

Total LOC (all files)
LOC by language (Python: 5000, JavaScript: 3000, etc.)
Code vs. Comments vs. Blank lines
LOC by directory/module
Language distribution (%)

Acceptance Criteria:

Integrate cloc as subprocess
Parse cloc JSON output
Visualize language distribution as pie chart
Track LOC growth over time (git history)
Ignore generated code and vendor directories

FR-2.2: Complexity Analysis

Tools:

Cyclomatic Complexity: radon (Python), eslint-complexity (JS)
Cognitive Complexity: SonarQube metrics
Halstead Metrics: Volume, difficulty, effort

Metrics:

Average cyclomatic complexity per function
High-complexity hotspots (>10 complexity)
Code maintainability index
Estimated development time based on complexity

Acceptance Criteria:

Calculate cyclomatic complexity for Python/JS/Java/Go
Identify top 10 most complex files
Apply complexity multiplier to COCOMO estimates
Visualize complexity distribution histogram
Flag files with complexity >15 as high-risk

FR-2.3: Dependency Analysis

Tools:

Python: pipdeptree
JavaScript: npm ls, yarn why
Java: mvn dependency:tree

Metrics:

Number of external dependencies
Dependency depth (direct vs. transitive)
Outdated dependencies (security risk)
License compliance

Acceptance Criteria:

Count total dependencies by ecosystem
Identify deprecated or unmaintained deps
Estimate integration effort (1 hour per new dependency)
Flag GPL/copyleft licenses for legal review

FR-3: Integration with External Systems

FR-3.1: GitHub API Integration

Purpose: Import actual data for estimate validation

Capabilities:

Pull Requests: Analyze merged PR stats (lines changed, review time)
Commits: Count commits, authors, commit frequency
Issues: Track issue resolution time
Velocity: Calculate team velocity from closed issues

Acceptance Criteria:

Authenticate with GitHub Personal Access Token
Fetch repository statistics (LOC, contributors, activity)
Calculate average PR cycle time
Import issues with labels as story points
Track estimate vs. actual (issue estimate vs. PR LOC)

FR-3.2: JIRA/Linear Integration

Purpose: Import story points and sprint velocity

Capabilities:

Stories: Import user stories with story point estimates
Sprints: Fetch sprint velocity (points completed per sprint)
Epics: Aggregate story points by epic
Velocity Trends: Chart velocity over last 6 sprints

Acceptance Criteria:

Authenticate with JIRA API token
Fetch all stories in a project with story points
Calculate team velocity from completed sprints
Import epic hierarchy for WBS
Sync estimates back to JIRA (optional)

FR-4: Reporting & Visualization

Required Report Types:

FR-4.1: Budget Estimate Report

Contents:

Project summary (name, size, timeline)
Estimation methodology used
Input parameters and assumptions
Cost breakdown by role/phase
Total cost with confidence interval
Risk factors and mitigation

Format: PDF, Markdown, HTML

Acceptance Criteria:

Professional formatting with company logo
Executive summary (1 page)
Detailed breakdown (3-5 pages)
Assumptions and disclaimers
Signature block for approval

FR-4.2: Timeline Projection (Gantt Chart)

Contents:

Project phases as horizontal bars
Task dependencies (arrows)
Critical path highlighted
Milestones as diamonds
Resource allocation

Format: Interactive HTML, PNG image, Microsoft Project XML

Acceptance Criteria:

Render Gantt chart with D3.js or Mermaid
Show critical path in red
Support task dependencies (finish-to-start, start-to-start)
Export to MS Project for editing
Responsive design for mobile viewing

FR-4.3: Risk Analysis Report

Contents:

Risk factors identified (complexity, team, schedule)
Probability × Impact matrix
Sensitivity analysis (which factors matter most)
Mitigation recommendations
Confidence intervals

Format: PDF, Interactive Dashboard

Acceptance Criteria:

Calculate risk score (0-100)
Visualize probability distribution
Identify top 5 risk drivers
Provide mitigation guidance
Track risk over project lifecycle

FR-4.4: Estimate vs. Actual Comparison

Purpose: Track estimation accuracy for continuous improvement

Contents:

Original estimate vs. actual cost/time
Variance % (over/under)
Breakdown by phase/module
Root cause analysis (what was missed)
Lessons learned

Format: PDF, Dashboard

Acceptance Criteria:

Require actual data input at project completion
Calculate variance % for effort, cost, duration
Identify patterns (always underestimate testing?)
Update parametric model with actuals
Track accuracy trends over time

FR-4.5: Export Formats

Required Exports:

PDF: Professional reports for executives
Excel: Editable budgets for finance teams
JSON: API integration with other tools
Markdown: Version-controlled estimates in git
CSV: Data export for analysis
MS Project XML: Timeline import

Acceptance Criteria:

All reports support PDF export
Excel export with formulas intact
JSON follows OpenAPI schema
Markdown renders on GitHub
CSV with proper escaping

Technical Requirements

TR-1: Technology Stack

TR-1.1: Calculation Engine (Backend)

Language: Python 3.10+

Rationale:

Excellent scientific computing libraries (NumPy, SciPy)
Mature data manipulation (Pandas)
Fast development for complex calculations
Easy integration with AI/ML for parametric models

Key Libraries:

NumPy: Matrix calculations, statistical functions
Pandas: Data manipulation, historical database
SciPy: Regression analysis, optimization
scikit-learn: Machine learning for parametric models
Pydantic: Data validation and schemas

Acceptance Criteria:

Python 3.10+ compatible
Type hints throughout codebase
80%+ test coverage with pytest
No dependencies on proprietary software

TR-1.2: REST API (Backend)

Framework: FastAPI

Rationale:

Modern async Python framework
Auto-generated OpenAPI 3.1 docs
Fast performance (comparable to Node.js)
Built-in validation with Pydantic
Easy deployment (Docker, Uvicorn)

API Design:

RESTful endpoints (/estimates, /projects, /reports)
JWT authentication for multi-user
Rate limiting for API abuse prevention
Versioned API (/api/v1/)
CORS support for web UI

Acceptance Criteria:

OpenAPI 3.1 spec auto-generated
All endpoints documented with examples
<200ms response time for estimates
Support 100+ concurrent requests
Comprehensive error handling

TR-1.3: Web UI (Frontend)

Framework: React 18 + TypeScript

Rationale:

Component reusability (estimation forms, charts)
Strong typing with TypeScript
Large ecosystem for charting (Recharts, D3.js)
CODITECT standard for frontend

UI Components:

Forms: Estimation input wizards
Charts: Gantt charts, pie charts, line charts
Tables: WBS hierarchies, cost breakdowns
Exports: PDF generation (jsPDF), Excel (SheetJS)

Acceptance Criteria:

Responsive design (mobile-friendly)
Accessible (WCAG 2.1 Level AA)
Dark mode support
<3s initial load time
Offline support (PWA) for saved estimates

TR-1.4: CLI Tool

Framework: Python Click

Rationale:

Quick estimates from command line
Integration with CI/CD pipelines
Scriptable for automation
Lightweight (no UI dependencies)

Commands:

# Estimate from codebase
coditect-estimate analyze ./src --method cocomo

# Estimate from WBS file
coditect-estimate wbs tasks.csv --output budget.pdf

# Import from JIRA
coditect-estimate jira PROJECT-123 --method velocity

# Generate report
coditect-estimate report estimate-id-456 --format pdf

Acceptance Criteria:

Help text for all commands
Support piped input/output
Exit codes for CI/CD integration
Progress bars for long operations
Config file support (~/.coditect/estimate.yaml)

TR-1.5: Database

Primary: SQLite (for local/embedded use) Optional: PostgreSQL (for cloud multi-user)

Rationale:

SQLite: Zero-config, embedded, fast for single-user
PostgreSQL: Full ACID, multi-user, cloud-ready

Schema:

projects: id, name, created_at, updated_at, status
estimates: id, project_id, method, inputs (JSON), outputs (JSON)
actuals: id, project_id, actual_effort, actual_cost, actual_duration
historical_data: Aggregated metrics for parametric models

Acceptance Criteria:

SQLite for CLI and standalone use
PostgreSQL support for cloud backend
Alembic migrations for schema changes
Backup/restore functionality
Data export as JSON for portability

TR-2: Performance Requirements

Metric	Requirement	Measurement
Estimation Latency	<2s for typical project	Time from API request to response
Codebase Analysis	<30s for 100K LOC	Time for `cloc` + complexity analysis
Report Generation	<10s for PDF export	Time to generate 10-page report
API Throughput	100+ requests/sec	Load testing with Locust
Database Query	<100ms for historical lookup	Query response time
UI Responsiveness	<200ms for input validation	Time to validate form field

Acceptance Criteria:

Load test with 1000 concurrent users
Profile slow endpoints with cProfile
Cache frequently accessed data (Redis optional)
Optimize database queries (indexes, explain plans)
Lazy load UI components

TR-3: Security Requirements

Authentication:

JWT tokens for API access
Session-based auth for web UI
API keys for CLI integration

Authorization:

Role-based access control (Admin, PM, Developer, Viewer)
Project-level permissions (who can view/edit estimates)

Data Protection:

Encrypt sensitive data at rest (SQLCipher for SQLite)
HTTPS required for all API calls
No logging of sensitive inputs
Regular security audits (npm audit, safety)

Acceptance Criteria:

All endpoints require authentication
Passwords hashed with bcrypt
HTTPS enforced (HSTS header)
Rate limiting (100 requests/min per user)
No SQL injection vulnerabilities
OWASP Top 10 compliance

TR-4: Scalability Requirements

Horizontal Scaling:

Stateless API (can run multiple instances)
Load balancer support (Nginx, HAProxy)
Database connection pooling

Vertical Scaling:

Efficient memory usage (<500MB per process)
CPU optimization (multiprocessing for analysis)
Disk space limits (historical data pruning)

Data Volume:

Support 10,000+ projects in database
1,000+ estimates per project
100MB+ historical data

Acceptance Criteria:

Tested with 10K projects in database
Response time <2s even with 10K projects
Pagination for large result sets
Database indexes on common queries
Background jobs for long-running analysis

TR-5: Deployment & Operations

Deployment Options:

Local/CLI: Pip install, runs on developer machine
Docker: Single container with SQLite
Docker Compose: Multi-container with PostgreSQL
Kubernetes: Cloud deployment with auto-scaling
Cloud (Optional): Hosted on coditect-cloud-backend

Monitoring:

Prometheus metrics (request count, latency, errors)
Structured logging (JSON logs)
Health check endpoint (/health)
Error tracking (Sentry integration optional)

Acceptance Criteria:

Dockerfile with <500MB image size
Docker Compose for local development
Kubernetes YAML manifests
Health check returns 200 OK in <1s
Logs include request ID for tracing

Use Cases & User Stories

UC-1: Estimate New Project from Requirements

Actor: Project Manager (Sarah) Goal: Get a budget estimate before development starts

Preconditions:

Sarah has project requirements document
She knows desired estimation method (Bottom-Up WBS)

Main Flow:

Sarah opens CODITECT estimation engine web UI
She clicks "New Estimate" and selects "Bottom-Up (WBS)"
She enters project name: "Customer Portal Redesign"

She creates WBS hierarchy:

Customer Portal Redesign
├── Phase 1: Design
│   ├── Wireframes (8 hours, Designer)
│   ├── UI Mockups (16 hours, Designer)
│   └── Design Review (4 hours, PM)
├── Phase 2: Frontend Development
│   ├── Login Module (24 hours, Senior Dev)
│   ├── Dashboard (32 hours, Mid Dev)
│   └── Settings Page (16 hours, Mid Dev)
├── Phase 3: Backend API
│   ├── Auth Service (40 hours, Senior Dev)
│   ├── User API (24 hours, Mid Dev)
│   └── Database Schema (16 hours, Senior Dev)
└── Phase 4: Testing & Deployment
    ├── Unit Tests (32 hours, QA)
    ├── Integration Tests (24 hours, QA)
    └── Deployment (16 hours, DevOps)

System calculates:
- Direct Effort: 252 hours
- Overhead (40%): 101 hours
- Contingency (15%): 53 hours
- Total: 406 hours
Sarah sets blended rate at $120/hour
System calculates Total Cost: $48,720
Sarah generates PDF budget report
She emails report to CTO for approval

Postconditions:

Estimate saved in database
PDF report generated
Sarah can track actual vs. estimate later

Alternative Flows:

A1: Sarah imports WBS from CSV instead of manual entry
A2: System suggests overhead percentages based on historical data
A3: CTO requests changes; Sarah edits estimate and regenerates report

Acceptance Criteria:

Complete estimate in <30 minutes
PDF report is professional and clear
Estimate is saved and retrievable
Can edit and regenerate report

User Story:

As a Project Manager,
I want to create bottom-up estimates from a WBS,
So that I can generate accurate budgets for executive approval.

Acceptance:
- WBS editor supports unlimited nesting
- Overhead factors auto-suggested from history
- PDF export in <10 seconds
- Estimate saved in database

UC-2: Validate Vendor Quote with COCOMO

Actor: CTO (Marcus) Goal: Verify if vendor's quote is reasonable

Preconditions:

Vendor provided quote: $180K for 50K LOC Java application
Marcus has access to estimation engine

Main Flow:

Marcus receives vendor quote for $180K
He opens estimation engine CLI

He runs:

coditect-estimate cocomo \
  --kloc 50 \
  --language java \
  --complexity average \
  --team-experience high \
  --output vendor-validation.pdf

System calculates using COCOMO II:
- Size: 50 KLOC
- Effort Multipliers: Complexity (1.2), Team Exp (0.85)
- Scale Factors: Team Cohesion (4), Process Maturity (3)
- Estimated Effort: 140 person-months
- Estimated Cost: $168K (at $100/hour blended rate)
System generates report showing:
- Estimated cost: $168K (±20% = $134K - $202K)
- Vendor quote: $180K
- Assessment: Within reasonable range ✅
Marcus approves vendor quote with confidence

Postconditions:

Marcus has data-backed validation
Report saved for future reference
Can negotiate if quote was outside range

Alternative Flows:

A1: Vendor quote is $250K (49% over estimate) → Marcus negotiates or finds another vendor
A2: Marcus doesn't know KLOC → Uses function point method instead

Acceptance Criteria:

COCOMO calculation matches reference tools
Confidence interval clearly displayed
Report explains assumptions
CLI completes in <5 seconds

User Story:

As a CTO,
I want to validate vendor quotes with COCOMO,
So that I can ensure we're not overpaying for development.

Acceptance:
- CLI accepts KLOC, language, complexity
- Report generated in <5s
- Confidence interval shown (e.g., ±20%)
- Clear pass/fail assessment

UC-3: Track Team Velocity for Sprint Planning

Actor: Developer (Alex) Goal: Estimate sprint capacity based on historical velocity

Preconditions:

Team has completed 6 sprints
JIRA integration configured

Main Flow:

Alex opens sprint planning meeting
Product owner presents 18 stories totaling 75 story points

Alex runs:

coditect-estimate jira \
  --project PROJ-123 \
  --method velocity \
  --sprints 6

System fetches from JIRA:
- Sprint 1: 28 points completed
- Sprint 2: 32 points completed
- Sprint 3: 26 points completed
- Sprint 4: 30 points completed
- Sprint 5: 34 points completed
- Sprint 6: 31 points completed
System calculates:
- Average Velocity: 30 points/sprint
- Standard Deviation: 3 points
- Recommended Commitment: 27-33 points (90% confidence)
Alex reports to team: "We can commit to 30 points this sprint"
Team selects top 30 points of work from backlog

Postconditions:

Team has realistic commitment
Stakeholders have accurate timeline (75 points / 30 per sprint = 3 sprints)

Alternative Flows:

A1: Velocity is declining → System warns of potential issues
A2: New team member joining → System suggests reducing commitment

Acceptance Criteria:

JIRA API fetches last N sprints
Velocity trend chart displayed
Recommended commitment shown with confidence
Can export as chart for stakeholders

User Story:

As a Developer,
I want to calculate team velocity from JIRA,
So that we can make realistic sprint commitments.

Acceptance:
- Fetch velocity from last 6 sprints
- Calculate average and std deviation
- Show velocity trend chart
- Recommend commitment with confidence level

UC-4: Generate Customer Quote from Requirements

Actor: Sales Engineer (Jordan) Goal: Create SOW with pricing for customer RFP

Preconditions:

Customer provided requirements document
Company standard rates configured ($150/hour)

Main Flow:

Jordan receives RFP for "E-commerce Marketplace MVP"
He analyzes requirements and counts:
- 15 external inputs (forms)
- 12 external outputs (reports)
- 8 external inquiries (searches)
- 6 internal files (database tables)
- 3 external interfaces (payment gateway, shipping API, auth)
He opens estimation engine and selects "Function Point Analysis"
He enters function point counts with complexity ratings
System calculates:
- Unadjusted Function Points: 245
- Complexity Adjustment: 1.15 (above average complexity)
- Adjusted Function Points: 282
System converts to effort:
- Language: Python/React
- Productivity: 25 hours/FP
- Estimated Effort: 7,050 hours
Jordan applies company rate:
- Effort: 7,050 hours
- Rate: $150/hour
- Quoted Price: $1,057,500
He adds 15% contingency: Final Quote: $1,216,000
System generates SOW PDF with:
- Functional requirements summary
- Function point breakdown
- Timeline: 9 months (7,050 hours / 40 hours/week / 5 devs = 35 weeks)
- Payment milestones (25% upfront, 25% at design complete, 25% at beta, 25% at launch)
Jordan presents to customer and wins deal

Postconditions:

Professional SOW generated
Pricing is competitive and profitable
Timeline is realistic

Alternative Flows:

A1: Customer negotiates → Jordan reduces scope to hit budget
A2: Customer requests fixed-price → Jordan adds 25% contingency

Acceptance Criteria:

Function point calculator with guided wizard
SOW template with company branding
Payment milestone options
Timeline as Gantt chart
PDF generation in <10s

User Story:

As a Sales Engineer,
I want to generate customer quotes from function points,
So that I can respond to RFPs quickly with accurate pricing.

Acceptance:
- Function point wizard (guided questions)
- Auto-convert FP to effort and cost
- SOW template with timeline and milestones
- Professional PDF in <10s

UC-5: Improve Estimates with Historical Data

Actor: Project Manager (Sarah) Goal: Use past project data to calibrate new estimates

Preconditions:

15 completed projects in database
Sarah is estimating a similar new project

Main Flow:

Sarah starts new estimate for "Mobile App - Fitness Tracker"
She selects "Parametric Model (Historical)"
System prompts for project attributes:
- Domain: Mobile App
- Platform: iOS + Android
- Team Size: 3 developers
- Size Estimate: 20K LOC

System searches historical database for similar projects:

Filters: Domain = Mobile App, Team Size = 2-5

Finds 5 matching projects:

Project A: 18K LOC, 4 devs, 1200 hours actual
Project B: 25K LOC, 3 devs, 1680 hours actual
Project C: 15K LOC, 2 devs, 960 hours actual
Project D: 22K LOC, 4 devs, 1440 hours actual
Project E: 19K LOC, 3 devs, 1260 hours actual

System builds linear regression model:
- Formula: Effort = 45 + (60 × KLOC)
- R² = 0.89 (good fit)
System predicts for 20K LOC project:
- Estimated Effort: 1,245 hours (±15%)
- Confidence Interval: 1,060 - 1,430 hours
Sarah reviews similar projects and trusts the estimate
She generates budget using parametric estimate
After project completes (actual: 1,310 hours), system updates model

Postconditions:

Estimate is calibrated to company's historical performance
Model improves with each completed project
Sarah has confidence in accuracy

Alternative Flows:

A1: No similar projects found → System falls back to COCOMO
A2: Model fit is poor (R² < 0.5) → System warns and suggests manual review

Acceptance Criteria:

Historical database with 10+ projects
Similarity filters (domain, size, team)
Regression model with R² displayed
Confidence interval based on model fit
Model auto-updates with new actuals

User Story:

As a Project Manager,
I want to estimate using historical project data,
So that my estimates reflect our team's actual performance.

Acceptance:
- Search historical projects by similarity
- Build regression model from 5+ projects
- Show confidence interval based on fit
- Auto-update model with new actuals

Data Model

Entity-Relationship Diagram (ERD)

┌─────────────────┐
│    projects     │
├─────────────────┤
│ id (PK)         │
│ name            │
│ description     │
│ domain          │
│ status          │
│ created_at      │
│ updated_at      │
│ created_by_id   │◄────┐
└─────────────────┘     │
        │               │
        │ 1:N           │
        ▼               │
┌─────────────────┐     │
│   estimates     │     │
├─────────────────┤     │
│ id (PK)         │     │
│ project_id (FK) │     │
│ method          │     │
│ inputs (JSON)   │     │
│ outputs (JSON)  │     │
│ created_at      │     │
│ created_by_id   │─────┘
└─────────────────┘
        │
        │ 1:1
        ▼
┌─────────────────┐
│    actuals      │
├─────────────────┤
│ id (PK)         │
│ estimate_id(FK) │
│ actual_effort   │
│ actual_cost     │
│ actual_duration │
│ variance_%      │
│ lessons_learned │
│ completed_at    │
└─────────────────┘

┌─────────────────┐
│      users      │
├─────────────────┤
│ id (PK)         │
│ email           │
│ password_hash   │
│ role            │
│ created_at      │
└─────────────────┘

┌─────────────────┐
│historical_data  │
├─────────────────┤
│ id (PK)         │
│ project_id (FK) │
│ domain          │
│ language        │
│ platform        │
│ team_size       │
│ kloc            │
│ function_points │
│ effort_hours    │
│ duration_weeks  │
│ cost_usd        │
│ year            │
└─────────────────┘

Schema Details

Table: `projects`

Column	Type	Constraints	Description
`id`	UUID	PRIMARY KEY	Unique project identifier
`name`	VARCHAR(255)	NOT NULL	Project name
`description`	TEXT	NULLABLE	Project description
`domain`	VARCHAR(50)	NULLABLE	Domain (web, mobile, desktop, embedded, etc.)
`status`	ENUM	NOT NULL	Status (planning, active, completed, archived)
`created_at`	TIMESTAMP	NOT NULL	Creation timestamp
`updated_at`	TIMESTAMP	NOT NULL	Last update timestamp
`created_by_id`	UUID	FOREIGN KEY	User who created project

Indexes:

idx_projects_status on status
idx_projects_created_by on created_by_id

Table: `estimates`

Column	Type	Constraints	Description
`id`	UUID	PRIMARY KEY	Unique estimate identifier
`project_id`	UUID	FOREIGN KEY	Associated project
`method`	VARCHAR(50)	NOT NULL	Estimation method (cocomo, wbs, velocity, function_points, three_point, parametric)
`inputs`	JSON	NOT NULL	Estimation inputs (method-specific)
`outputs`	JSON	NOT NULL	Estimation results (effort, cost, duration, etc.)
`created_at`	TIMESTAMP	NOT NULL	Creation timestamp
`created_by_id`	UUID	FOREIGN KEY	User who created estimate

Indexes:

idx_estimates_project_id on project_id
idx_estimates_method on method

Inputs JSON Schema (COCOMO example):

{
  "size_kloc": 50,
  "language": "java",
  "scale_factors": {
    "precedentedness": 3,
    "development_flexibility": 4,
    "architecture_risk_resolution": 3,
    "team_cohesion": 4,
    "process_maturity": 3
  },
  "effort_multipliers": {
    "required_reliability": 1.0,
    "database_size": 1.2,
    "product_complexity": 1.3,
    "analyst_capability": 0.85,
    "programmer_capability": 0.9
  }
}

Outputs JSON Schema:

{
  "effort_person_months": 140,
  "effort_hours": 22400,
  "duration_months": 18,
  "team_size": 8,
  "cost_usd": 168000,
  "confidence_interval": {
    "low": 134000,
    "high": 202000
  }
}

Table: `actuals`

Column	Type	Constraints	Description
`id`	UUID	PRIMARY KEY	Unique actual identifier
`estimate_id`	UUID	FOREIGN KEY	Associated estimate
`actual_effort`	INTEGER	NOT NULL	Actual effort in hours
`actual_cost`	DECIMAL(10,2)	NOT NULL	Actual cost in USD
`actual_duration`	INTEGER	NOT NULL	Actual duration in days
`variance_percent`	DECIMAL(5,2)	COMPUTED	(actual - estimate) / estimate * 100
`lessons_learned`	TEXT	NULLABLE	Post-mortem notes
`completed_at`	TIMESTAMP	NOT NULL	Project completion date

Indexes:

idx_actuals_estimate_id on estimate_id

Table: `users`

Column	Type	Constraints	Description
`id`	UUID	PRIMARY KEY	Unique user identifier
`email`	VARCHAR(255)	UNIQUE, NOT NULL	User email
`password_hash`	VARCHAR(255)	NOT NULL	Bcrypt hashed password
`role`	ENUM	NOT NULL	Role (admin, pm, developer, viewer)
`created_at`	TIMESTAMP	NOT NULL	Account creation date

Indexes:

idx_users_email on email

Table: `historical_data`

Column	Type	Constraints	Description
`id`	UUID	PRIMARY KEY	Unique record identifier
`project_id`	UUID	FOREIGN KEY	Associated project (if applicable)
`domain`	VARCHAR(50)	NOT NULL	Domain (web, mobile, etc.)
`language`	VARCHAR(50)	NOT NULL	Primary language (python, java, etc.)
`platform`	VARCHAR(50)	NULLABLE	Platform (cloud, on-prem, etc.)
`team_size`	INTEGER	NOT NULL	Number of developers
`kloc`	DECIMAL(10,2)	NULLABLE	Lines of code (thousands)
`function_points`	INTEGER	NULLABLE	Function points
`effort_hours`	INTEGER	NOT NULL	Total effort in hours
`duration_weeks`	INTEGER	NOT NULL	Total duration in weeks
`cost_usd`	DECIMAL(10,2)	NOT NULL	Total cost in USD
`year`	INTEGER	NOT NULL	Year project completed

Indexes:

idx_historical_domain on domain
idx_historical_language on language
idx_historical_team_size on team_size

Populated with:

Internal AZ1.AI projects
Anonymized customer projects (with permission)
Public datasets (ISBSG, NASA COCOMO)

API Specification

OpenAPI 3.1 Overview

Base URL: https://api.coditect.ai/estimation/v1 Authentication: Bearer token (JWT) Content-Type: application/json

Endpoints

POST `/estimates`

Purpose: Create new estimate

Request Body:

{
  "project_id": "uuid",
  "method": "cocomo" | "wbs" | "velocity" | "function_points" | "three_point" | "parametric",
  "inputs": {
    // Method-specific inputs (see data model)
  }
}

Response (201 Created):

{
  "estimate_id": "uuid",
  "project_id": "uuid",
  "method": "cocomo",
  "outputs": {
    "effort_hours": 22400,
    "cost_usd": 168000,
    "duration_months": 18,
    "team_size": 8,
    "confidence_interval": {
      "low": 134000,
      "high": 202000
    }
  },
  "created_at": "2025-11-22T10:30:00Z"
}

Error Codes:

400 Bad Request - Invalid inputs
401 Unauthorized - Missing/invalid token
404 Not Found - Project not found

GET `/estimates/{estimate_id}`

Purpose: Retrieve existing estimate

Response (200 OK):

{
  "estimate_id": "uuid",
  "project_id": "uuid",
  "method": "cocomo",
  "inputs": { /* ... */ },
  "outputs": { /* ... */ },
  "created_at": "2025-11-22T10:30:00Z"
}

POST `/estimates/{estimate_id}/actuals`

Purpose: Record actual results for completed project

Request Body:

{
  "actual_effort": 24000,
  "actual_cost": 185000,
  "actual_duration": 20,
  "lessons_learned": "Underestimated database complexity"
}

Response (201 Created):

{
  "actual_id": "uuid",
  "estimate_id": "uuid",
  "variance_percent": 10.5,
  "completed_at": "2025-11-22T10:30:00Z"
}

POST `/analyze/codebase`

Purpose: Analyze existing codebase for LOC and complexity

Request Body:

{
  "repository_url": "https://github.com/user/repo",
  "branch": "main"
}

Response (200 OK):

{
  "total_loc": 125000,
  "languages": {
    "python": 75000,
    "javascript": 35000,
    "html": 15000
  },
  "complexity": {
    "average_cyclomatic": 5.2,
    "high_complexity_files": 12
  },
  "estimated_kloc": 125
}

POST `/reports/{estimate_id}/generate`

Purpose: Generate report in specified format

Request Body:

{
  "format": "pdf" | "excel" | "markdown" | "json",
  "report_type": "budget" | "timeline" | "risk" | "comparison"
}

Response (200 OK):

{
  "report_url": "https://api.coditect.ai/reports/uuid.pdf",
  "expires_at": "2025-11-29T10:30:00Z"
}

GET `/historical`

Purpose: Query historical project data for parametric models

Query Parameters:

domain: Filter by domain (web, mobile, etc.)
language: Filter by language
team_size_min: Minimum team size
team_size_max: Maximum team size
year_min: Minimum year

Response (200 OK):

{
  "projects": [
    {
      "id": "uuid",
      "domain": "web",
      "language": "python",
      "team_size": 4,
      "kloc": 50,
      "effort_hours": 8000,
      "duration_weeks": 24,
      "cost_usd": 120000,
      "year": 2024
    }
  ],
  "count": 15,
  "regression_model": {
    "formula": "effort = 45 + (60 * kloc)",
    "r_squared": 0.89
  }
}

Rate Limiting

Free Tier: 100 requests/day
Paid Tier: 10,000 requests/day
Enterprise: Unlimited

Headers:

X-RateLimit-Limit: Total requests allowed
X-RateLimit-Remaining: Requests remaining
X-RateLimit-Reset: Timestamp when limit resets

Reporting Requirements

Report 1: Budget Estimate Report

Template: Professional PDF with AZ1.AI/CODITECT branding

Sections:

Executive Summary (1 page)
- Project name and description
- Total cost estimate with confidence interval
- Timeline estimate
- Key assumptions and risks
Estimation Methodology (0.5 page)
- Method used (e.g., COCOMO II)
- Why this method was chosen
- Input parameters
Cost Breakdown (1-2 pages)
- By role (developer, QA, PM, etc.)
- By phase (design, development, testing, deployment)
- By module/feature
- Overhead factors (testing, PM, infrastructure, rework)
Timeline Projection (1 page)
- Total duration (weeks/months)
- Key milestones
- Critical path items
- Resource allocation
Risk Analysis (1 page)
- Risk factors identified
- Probability × Impact assessment
- Mitigation recommendations
- Contingency allocation
Assumptions & Disclaimers (0.5 page)
- Team composition assumptions
- Technology stack assumptions
- Availability assumptions
- Exclusions (what's not included)
Appendix (optional)
- Detailed WBS
- Historical data references
- Sensitivity analysis

Format:

PDF (primary)
Markdown (for version control)
HTML (for embedding in dashboards)

Branding:

AZ1.AI logo in header
CODITECT footer
Professional color scheme (blue/gray)

Report 2: Timeline Projection (Gantt Chart)

Visualization: Interactive Gantt chart

Features:

Horizontal bars for tasks/phases
Dependencies shown as arrows
Critical path highlighted in red
Milestones as diamonds
Resource allocation shown
Today marker (current date line)

Interactivity (Web UI):

Hover to see task details
Click to edit (if permissions allow)
Zoom in/out on timeline
Filter by resource/phase

Export Formats:

PNG image (for presentations)
HTML (interactive, embeddable)
Microsoft Project XML (for editing in MS Project)
JSON (for API integration)

Libraries:

D3.js or Mermaid for rendering
FullCalendar or Gantt-task-react

Report 3: Risk Analysis Report

Purpose: Identify and quantify project risks

Sections:

Risk Score Summary
- Overall risk score (0-100)
- Risk level (Low/Medium/High)
- Comparison to similar projects
Risk Factors
- Technical complexity
- Team experience
- Schedule pressure
- Requirement uncertainty
- External dependencies
Probability × Impact Matrix
- Visual matrix (4x4 grid)
- Risks plotted by probability and impact
- Color-coded (green/yellow/red)
Sensitivity Analysis
- Tornado chart showing factor impact
- "What-if" scenarios
- Most influential factors highlighted
Mitigation Recommendations
- Top 5 risks with mitigation strategies
- Contingency allocation
- Risk monitoring plan

Visualization:

Probability distribution curve
Monte Carlo simulation results
Risk heatmap

Report 4: Estimate vs. Actual Comparison

Purpose: Track estimation accuracy for continuous improvement

Sections:

Variance Summary
- Original estimate vs. actual
- Variance % (over/under)
- Breakdown by effort, cost, duration
Breakdown by Phase/Module
- Table showing estimate vs. actual for each phase
- Identify where estimation was off
Root Cause Analysis
- What was underestimated? (testing, complexity, rework)
- What was overestimated? (learning curve, reuse)
- Patterns across projects
Lessons Learned
- Free-form notes from PM
- Recommendations for future estimates
Historical Accuracy Trends
- Chart showing accuracy over time
- Goal: Improve from ±50% to ±20%

Update Workflow:

PM marks project complete
Enters actual effort/cost/duration
System calculates variance
PM writes lessons learned
Data added to historical database

Report 5: Export Formats

All reports support:

PDF: Professional documents for executives
Excel: Editable spreadsheets for finance teams
JSON: API integration with other tools
Markdown: Version-controlled estimates in git
CSV: Data export for analysis

Export Features:

Preserve formatting (headers, colors, logos)
Include metadata (created date, author, version)
Compress large exports (ZIP)
Password-protect sensitive reports (optional)

Integration Plan

Integration with CODITECT Ecosystem

INT-1: Slash Command (`/estimate`)

Purpose: Quick estimation from Claude Code workflow

Usage:

/estimate cocomo --kloc 50 --language java
/estimate wbs --file tasks.csv
/estimate jira PROJECT-123

Implementation:

Add to coditect-core/commands/estimate.md
CLI wrapper calls estimation engine API or local script
Output formatted as Markdown in Claude response

Acceptance Criteria:

Command registered in CODITECT commands/
Help text with examples
Output is human-readable
Integrates with current project context

INT-2: Skill (`estimation-calculator`)

Purpose: Reusable estimation patterns for other agents

Skill Definition:

name: estimation-calculator
category: project-management
description: |
  Calculate software project estimates using COCOMO, WBS, Function Points,
  Story Points, Three-Point, or Parametric models.
inputs:
  - method: Estimation method
  - project_attributes: Size, complexity, team, etc.
outputs:
  - effort: Estimated effort in hours
  - cost: Estimated cost in USD
  - duration: Estimated duration in weeks
  - confidence_interval: Range of possible outcomes

Usage by Agents:

project-manager agent uses for budget creation
budget-analyst agent uses for validation
sales-engineer agent uses for quote generation

INT-3: Agent (`budget-analyst`)

Purpose: Specialized agent for financial analysis and estimation

Agent Definition:

name: budget-analyst
category: finance
expertise:
  - Software cost estimation
  - Budget validation
  - Financial modeling
  - ROI analysis
capabilities:
  - Estimate project costs using multiple methods
  - Validate vendor quotes
  - Generate budget reports
  - Track estimate vs. actual
  - Recommend cost optimizations
tools:
  - estimation-calculator skill
  - /estimate command
  - Historical project database

Example Invocation:

Use budget-analyst subagent to create a budget estimate for
the Customer Portal Redesign project with 50K LOC Java codebase.

Agent Response:

Asks clarifying questions (team size, timeline, complexity)
Runs multiple estimation methods (COCOMO, historical)
Compares results and explains differences
Generates professional budget report
Recommends contingency percentage

Acceptance Criteria:

Agent definition in coditect-core/agents/budget-analyst.md
Responds to estimation requests
Uses estimation-calculator skill
Generates professional reports
Explains methodology clearly

INT-4: Script (`estimate-project.py`)

Purpose: CLI tool for estimation outside Claude workflow

Location: coditect-ops-estimation-engine/scripts/estimate-project.py

Usage:

# COCOMO estimation
python estimate-project.py cocomo \
  --kloc 50 \
  --language java \
  --output budget.pdf

# WBS estimation
python estimate-project.py wbs \
  --file tasks.csv \
  --rate 120 \
  --output budget.pdf

# Codebase analysis
python estimate-project.py analyze \
  --repo /path/to/repo \
  --method cocomo

Acceptance Criteria:

Supports all 6 estimation methods
CLI help text with examples
Output to PDF, JSON, CSV
Progress indicators for long operations
Exit codes for CI/CD integration

INT-5: Script (`analyze-codebase.py`)

Purpose: Automated LOC and complexity analysis

Usage:

# Analyze local repository
python analyze-codebase.py /path/to/repo

# Analyze GitHub repository
python analyze-codebase.py --github https://github.com/user/repo

# Output as JSON for API integration
python analyze-codebase.py /path/to/repo --format json

Output:

{
  "total_loc": 125000,
  "languages": {
    "python": 75000,
    "javascript": 35000,
    "html": 15000
  },
  "complexity": {
    "average_cyclomatic": 5.2,
    "high_complexity_files": [
      "src/engine/calculator.py (complexity: 18)",
      "src/api/routes.py (complexity: 15)"
    ]
  },
  "estimated_kloc": 125,
  "suggested_cocomo_inputs": {
    "size_kloc": 125,
    "complexity_multiplier": 1.2
  }
}

Acceptance Criteria:

Integrates cloc as subprocess
Parses and enriches output
Identifies high-complexity files
Suggests estimation inputs
Works on local and remote repos

Integration with External Systems

INT-6: GitHub API Integration

Purpose: Import codebase stats and PR data

Authentication: Personal Access Token (PAT)

Endpoints Used:

GET /repos/{owner}/{repo} - Repo metadata
GET /repos/{owner}/{repo}/stats/contributors - LOC by contributor
GET /repos/{owner}/{repo}/pulls - Pull request stats

Data Imported:

Total LOC (from contributors endpoint)
Language distribution
Commit frequency
PR cycle time (open to merge)

Acceptance Criteria:

OAuth or PAT authentication
Fetch repository statistics
Calculate average PR cycle time
Use for velocity estimation

INT-7: JIRA/Linear Integration

Purpose: Import story points and velocity

Authentication: API token

JIRA Endpoints:

GET /rest/api/3/search - Search for issues
GET /rest/agile/1.0/board/{boardId}/sprint - Get sprints
GET /rest/agile/1.0/sprint/{sprintId}/issue - Get issues in sprint

Data Imported:

User stories with story points
Sprint velocity (points completed per sprint)
Epic hierarchy

Acceptance Criteria:

JIRA Cloud API integration
Fetch story points from custom field
Calculate velocity from last 6 sprints
Support epic rollup

CODITECT Cloud Backend Integration (Optional)

Purpose: Store estimates in cloud for multi-user access

Architecture:

coditect-ops-estimation-engine (this repo)
    ↓ API calls (optional)
coditect-cloud-backend
    ↓ Database storage
PostgreSQL (cloud-hosted)

Benefits:

Multi-user access
Centralized historical database
Team collaboration on estimates
Cloud backup and sync

Implementation:

Estimation engine checks for cloud API URL in config
If present, syncs estimates to cloud
If absent, stores locally in SQLite

Acceptance Criteria:

Optional cloud sync configuration
Syncs estimates on save
Conflict resolution (local vs. cloud)
Offline mode with sync-on-reconnect

Success Metrics

Primary Metrics

Metric	Target	Measurement Method
Estimation Accuracy	±20% variance	Compare estimate vs. actual for 10+ projects
Time Savings	80% reduction	User survey: time to create estimate
User Adoption	50+ active users	Monthly active users in first 6 months
Report Generation	<10s for PDF	Performance monitoring
API Uptime	99.5%	Prometheus uptime tracking

Secondary Metrics

Metric	Target	Measurement Method
API Usage	1,000+ estimates/month	API request logs
Historical Database Growth	100+ projects in Year 1	Database record count
Customer Satisfaction	NPS >50	User survey
Bug Rate	<5 critical bugs/quarter	GitHub issue tracking
Documentation Completeness	100% API endpoints documented	OpenAPI spec coverage

Success Criteria by Phase

Phase 1: Alpha (Weeks 1-4)

All 6 estimation methods implemented
CLI tool functional
Basic API endpoints working
5+ internal projects estimated

Phase 2: Beta (Weeks 5-8)

Web UI functional
PDF reports generated
10+ beta testers using tool
Historical database with 20+ projects

Phase 3: Production (Weeks 9-12)

Public API launch
CODITECT integration complete
50+ active users
Documentation complete

Risk Assessment

Technical Risks

Risk	Probability	Impact	Mitigation
COCOMO calculation errors	Medium	High	Validate against reference tools (COPSEMO)
Codebase analysis failures	Medium	Medium	Fallback to manual LOC input
API performance issues	Low	High	Load testing, caching, horizontal scaling
Database scalability	Low	Medium	Partition historical data, add indexes
Integration bugs (JIRA/GitHub)	Medium	Medium	Comprehensive API mocking and testing

Business Risks

Risk	Probability	Impact	Mitigation
Low user adoption	Medium	High	Strong CODITECT integration, user training
Competitors with similar tools	High	Medium	Differentiate with AI agent integration
Inaccurate estimates damage reputation	Medium	High	Confidence intervals, clear disclaimers
Insufficient historical data	Medium	Medium	Seed with public datasets (ISBSG, NASA)
Pricing model unclear	Low	Medium	Start free for CODITECT users, enterprise upsell

Operational Risks

Risk	Probability	Impact	Mitigation
Maintenance burden	Medium	Medium	Automated testing, CI/CD, monitoring
Security vulnerabilities	Low	High	Regular audits, OWASP compliance
Data privacy concerns	Low	High	Encrypt sensitive data, GDPR compliance
Dependency on external APIs	Medium	Medium	Rate limiting, graceful degradation

Implementation Roadmap

Phase 1: Foundation (Weeks 1-4)

Goal: Core estimation engine with CLI

Deliverables:

Team:

1 Backend Developer (Python)
1 QA Engineer (Testing)

Duration: 4 weeks Budget: $40K (1 dev @ $150/hr × 160 hrs/month × 1 month + QA @ $100/hr × 80 hrs)

Phase 2: API & Web UI (Weeks 5-8)

Goal: REST API and web interface

Deliverables:

Team:

1 Backend Developer (FastAPI)
1 Frontend Developer (React)
1 QA Engineer

Duration: 4 weeks Budget: $60K (2 devs @ $150/hr × 160 hrs + QA @ $100/hr × 160 hrs)

Phase 3: Integrations (Weeks 9-12)

Goal: GitHub, JIRA, and CODITECT integrations

Deliverables:

Automated codebase analysis (cloc, tokei)
Complexity analysis (cyclomatic, Halstead)
GitHub API integration
JIRA/Linear API integration
CODITECT slash command (/estimate)
CODITECT skill (estimation-calculator)
CODITECT agent (budget-analyst)
Historical database seeding (50+ projects)

Team:

1 Integration Engineer (APIs)
1 CODITECT Specialist (Agent/Skills)
1 Data Engineer (Historical DB)

Duration: 4 weeks Budget: $50K (3 engineers @ $150/hr × 133 hrs each)

Phase 4: Reporting & Polish (Weeks 13-16)

Goal: Production-ready reports and deployment

Deliverables:

Budget estimate report (PDF)
Timeline Gantt chart (interactive)
Risk analysis report
Estimate vs. actual comparison
Export formats (Excel, JSON, CSV, Markdown)
Docker deployment
Kubernetes manifests
Monitoring (Prometheus, Grafana)
Documentation (user guide, API reference)

Team:

1 Backend Developer (Reporting)
1 Frontend Developer (Visualizations)
1 DevOps Engineer (Deployment)
1 Technical Writer (Docs)

Duration: 4 weeks Budget: $70K (4 people @ $150/hr × 100 hrs each, tech writer @ $100/hr)

Total Implementation

Duration: 16 weeks (4 months) Budget: $220K Team: 4-6 engineers (rotating)

Budget & Resources

Development Budget Breakdown

Phase	Duration	Team	Cost
Phase 1: Foundation	4 weeks	1 Backend Dev + 1 QA	$40K
Phase 2: API & UI	4 weeks	1 Backend + 1 Frontend + 1 QA	$60K
Phase 3: Integrations	4 weeks	1 Integration + 1 CODITECT + 1 Data	$50K
Phase 4: Reporting	4 weeks	2 Devs + 1 DevOps + 1 Writer	$70K
Total Development	16 weeks	4-6 engineers	$220K

Ongoing Costs (Annual)

Category	Cost/Year	Notes
Cloud Hosting	$3,600	GCP: VM, database, storage
API Costs	$1,200	GitHub, JIRA API usage
Monitoring	$600	Prometheus Cloud, Grafana
Support & Maintenance	$30K	20% dev time for bugs/updates
Total Ongoing	$35,400/year

Resource Requirements

Engineers:

Backend Developer (Python): 320 hours
Frontend Developer (React): 160 hours
Integration Engineer: 160 hours
CODITECT Specialist: 160 hours
Data Engineer: 160 hours
DevOps Engineer: 160 hours
QA Engineer: 320 hours
Technical Writer: 80 hours

Total: 1,520 engineering hours over 16 weeks

Revenue Projection (Optional)

Pricing Model:

Free Tier: 10 estimates/month (for CODITECT users)
Pro Tier: $49/month - 100 estimates/month, advanced reports
Enterprise Tier: $499/month - Unlimited, multi-user, API access, cloud hosting

Projected Revenue (Year 1):

Month 1-3: Beta (free)
Month 4-6: 50 Pro users × $49 = $2,450/month
Month 7-12: 150 Pro users × $49 + 5 Enterprise × $499 = $9,845/month

Year 1 Total Revenue: ~$50K Break-Even: Month 18 (if standalone product)

Note: Likely bundled with CODITECT Core as value-add, not standalone revenue.

Appendices

Appendix A: Industry Research Sources

Estimation Methodologies:

COCOMO II: http://csse.usc.edu/csse/research/COCOMOII/cocomo_main.html
ISBSG: https://www.isbsg.org/ (historical data benchmarks)
Function Point Users Group (IFPUG): https://www.ifpug.org/
Agile Estimation (Story Points): Mike Cohn, "Agile Estimating and Planning"

Tools & Libraries:

cloc: https://github.com/AlDanial/cloc
tokei: https://github.com/XAMPPRocky/tokei
Radon (Python complexity): https://radon.readthedocs.io/
scikit-learn: https://scikit-learn.org/

Appendix B: Sample Historical Data

Dataset: NASA COCOMO Historical Projects (anonymized)

Project	Domain	Language	Team	KLOC	Effort (hrs)	Duration (weeks)	Cost (USD)
Project A	Embedded	C	5	30	12000	48	$180K
Project B	Web	Python	3	15	4500	24	$67K
Project C	Mobile	Swift	4	20	7200	32	$108K
Project D	Desktop	Java	6	50	18000	56	$270K
Project E	Web	JavaScript	2	10	3000	20	$45K

Usage: Seed historical database for parametric models

Appendix C: Glossary

COCOMO: Constructive Cost Model - Parametric software estimation model
KLOC: Thousands of Lines of Code
Function Points: Language-agnostic measure of software functionality
WBS: Work Breakdown Structure - Hierarchical task decomposition
Story Points: Relative measure of effort in Agile development
PERT: Program Evaluation and Review Technique - Three-point estimation
Cyclomatic Complexity: Measure of code complexity based on control flow
Halstead Metrics: Software complexity metrics (volume, difficulty, effort)
Blended Rate: Average hourly rate across all roles on project
Overhead: Indirect costs (testing, PM, infrastructure, rework)
Contingency: Risk buffer added to estimates

Document Control

Version History:

Version	Date	Author	Changes
1.0	2025-11-22	Hal Casteel	Initial requirements document

Approvals:

Role	Name	Signature	Date
CEO	Hal Casteel	_______________	_______
CTO	Hal Casteel	_______________	_______
Product Lead	___________	_______________	_______

Next Steps:

Review: Stakeholder review and approval (1 week)
Planning: Detailed technical design document (1 week)
Kickoff: Phase 1 implementation start (Week 3)

END OF DOCUMENT

Contact:

Document Owner: Hal Casteel (hal@az1.ai)
Repository: coditect-ops-estimation-engine
Organization: AZ1.AI INC
License: MIT (open-source after production launch)

Table of Contents​

Executive Summary​

Purpose​

Goals​

Success Criteria​

Value Proposition​

Problem Statement​

What Problem Does This Solve?​

Who Has This Problem?​

Why Does This Matter?​

Strategic Context​

Ecosystem Role​

Integration with CODITECT Platform​

Market Positioning​

User Personas​

Persona 1: Project Manager (Primary)​

Persona 2: CTO/VP Engineering (Primary)​

Persona 3: Founder/CEO (Secondary)​

Persona 4: Sales Engineer (Secondary)​

Persona 5: Developer (Tertiary)​

Functional Requirements​

FR-1: Estimation Methodologies​

FR-1.1: COCOMO II (Constructive Cost Model)​

FR-1.2: Bottom-Up Estimation (Work Breakdown Structure)​

FR-1.3: Story Points + Velocity​

FR-1.4: Function Point Analysis​

FR-1.5: Three-Point Estimation (PERT)​

FR-1.6: Parametric Models (Historical Regression)​

FR-2: Automated Codebase Analysis​

FR-2.1: Lines of Code (LOC) Counting​

FR-2.2: Complexity Analysis​

FR-2.3: Dependency Analysis​

FR-3: Integration with External Systems​

FR-3.1: GitHub API Integration​

FR-3.2: JIRA/Linear Integration​

FR-4: Reporting & Visualization​

FR-4.1: Budget Estimate Report​

FR-4.2: Timeline Projection (Gantt Chart)​

FR-4.3: Risk Analysis Report​

FR-4.4: Estimate vs. Actual Comparison​

FR-4.5: Export Formats​

Technical Requirements​

TR-1: Technology Stack​

TR-1.1: Calculation Engine (Backend)​

TR-1.2: REST API (Backend)​

TR-1.3: Web UI (Frontend)​

TR-1.4: CLI Tool​

TR-1.5: Database​

TR-2: Performance Requirements​

TR-3: Security Requirements​

TR-4: Scalability Requirements​

TR-5: Deployment & Operations​

Use Cases & User Stories​

UC-1: Estimate New Project from Requirements​

UC-2: Validate Vendor Quote with COCOMO​

UC-3: Track Team Velocity for Sprint Planning​

UC-4: Generate Customer Quote from Requirements​

UC-5: Improve Estimates with Historical Data​

Data Model​

Entity-Relationship Diagram (ERD)​

Schema Details​

Table: projects​

Table: estimates​

Table: actuals​

Table: users​

Table: historical_data​

API Specification​

OpenAPI 3.1 Overview​

Endpoints​

POST /estimates​

GET /estimates/{estimate_id}​

POST /estimates/{estimate_id}/actuals​

POST /analyze/codebase​

POST /reports/{estimate_id}/generate​

GET /historical​

Rate Limiting​

Reporting Requirements​

Report 1: Budget Estimate Report​

Report 2: Timeline Projection (Gantt Chart)​

Report 3: Risk Analysis Report​

Table of Contents

Executive Summary

Purpose

Goals

Success Criteria

Value Proposition

Problem Statement

What Problem Does This Solve?

Who Has This Problem?

Why Does This Matter?

Strategic Context

Ecosystem Role

Integration with CODITECT Platform

Market Positioning

User Personas

Persona 1: Project Manager (Primary)

Persona 2: CTO/VP Engineering (Primary)

Persona 3: Founder/CEO (Secondary)

Persona 4: Sales Engineer (Secondary)

Persona 5: Developer (Tertiary)

Functional Requirements

FR-1: Estimation Methodologies

FR-1.1: COCOMO II (Constructive Cost Model)

FR-1.2: Bottom-Up Estimation (Work Breakdown Structure)

FR-1.3: Story Points + Velocity

FR-1.4: Function Point Analysis

FR-1.5: Three-Point Estimation (PERT)

FR-1.6: Parametric Models (Historical Regression)

FR-2: Automated Codebase Analysis

FR-2.1: Lines of Code (LOC) Counting

FR-2.2: Complexity Analysis

FR-2.3: Dependency Analysis

FR-3: Integration with External Systems

FR-3.1: GitHub API Integration

FR-3.2: JIRA/Linear Integration

FR-4: Reporting & Visualization

FR-4.1: Budget Estimate Report

FR-4.2: Timeline Projection (Gantt Chart)

FR-4.3: Risk Analysis Report

FR-4.4: Estimate vs. Actual Comparison

FR-4.5: Export Formats

Technical Requirements

TR-1: Technology Stack

TR-1.1: Calculation Engine (Backend)

TR-1.2: REST API (Backend)

TR-1.3: Web UI (Frontend)

TR-1.4: CLI Tool

TR-1.5: Database

TR-2: Performance Requirements

TR-3: Security Requirements

TR-4: Scalability Requirements

TR-5: Deployment & Operations

Use Cases & User Stories

UC-1: Estimate New Project from Requirements

UC-2: Validate Vendor Quote with COCOMO

UC-3: Track Team Velocity for Sprint Planning

UC-4: Generate Customer Quote from Requirements

UC-5: Improve Estimates with Historical Data

Data Model

Entity-Relationship Diagram (ERD)

Schema Details

Table: `projects`

Table: `estimates`

Table: `actuals`

Table: `users`

Table: `historical_data`

API Specification

OpenAPI 3.1 Overview

Endpoints

POST `/estimates`

GET `/estimates/{estimate_id}`

POST `/estimates/{estimate_id}/actuals`

POST `/analyze/codebase`

POST `/reports/{estimate_id}/generate`

GET `/historical`

Rate Limiting

Reporting Requirements

Report 1: Budget Estimate Report

Report 2: Timeline Projection (Gantt Chart)

Report 3: Risk Analysis Report