Examples

This section presents practical implementations of Decision-Driven Software Engineering methodology, demonstrating how Technical Decision Records guide software development from strategic decisions to implementation details.

Available Examples

The DDSE Foundation provides two complementary examples based on the TaskFlow project, illustrating different aspects of the methodology:

TaskFlow Decision Documentation (task-app-tdr-only)

Purpose: Demonstrates comprehensive TDR creation as the foundation for AI-assisted development

Scope: Complete decision memory for a simple task management API designed for small teams (2-10 people)

TaskFlow Decision Documentation Framework

The TaskFlow examples demonstrate systematic implementation of DDSE methodology through a practical task management system. These examples illustrate the complete methodology from strategic decision documentation through implementation traceability.

Decision Documentation Example (task-app-tdr-only)

Methodological Focus: Comprehensive TDR creation as foundation for consistent development

Project Scope: Task management API for small teams with explicit time and complexity constraints

Technical Decision Hierarchy:

  • MDD-001: Product strategy defining essential functionality and deployment constraints
  • ADR-001: REST API architectural choice with JSON request/response patterns
  • ADR-002: SQLite data storage strategy with SQLAlchemy ORM integration
  • EDR-001: JWT authentication implementation with team-based authorization
  • EDR-002: Structured error handling approach with consistent response formats
  • IDR-001: API endpoint naming conventions following RESTful principles
  • IDR-002: Input validation patterns using Pydantic model specifications

AI Integration Framework: Each TDR includes structured AI context sections providing:

  • Implementation priority guidance directing development sequence
  • Framework-specific recommendations for technology application
  • Constraint enforcement rules ensuring architectural compliance
  • Validation requirements for automated quality assurance

Methodological Contribution: Demonstrates how systematic decision documentation creates reusable context for both human developers and AI tools, enabling consistent implementation across iterations.

Implementation Traceability Example (task-app-python)

Methodological Focus: Direct translation of documented decisions into maintainable code structures

Implementation Framework: FastAPI-based REST API implementing TaskFlow specifications

Decision-to-Code Traceability:

  • Strategic Alignment: Code structure directly reflects MDD-001 constraints for rapid deployment
  • Architectural Compliance: API endpoints implement ADR-001 RESTful conventions precisely
  • Data Layer Consistency: Database models follow ADR-002 SQLAlchemy patterns exactly
  • Security Implementation: Authentication follows EDR-001 JWT specifications completely
  • Error Handling: Response patterns implement EDR-002 structured error formats
  • API Conventions: Endpoint design follows IDR-001 naming standards systematically
  • Validation Patterns: Input handling implements IDR-002 Pydantic specifications

Code Reference System: Implementation includes explicit TDR references in comments, linking specific code sections to their governing decisions.

Quality Assurance: Includes automated validation demonstrating compliance with documented decision criteria.

Learning Through Examples

For Methodology Understanding

Decision Documentation Process: Study task-app-tdr-only to understand how technical decisions are systematically captured, structured, and connected in a hierarchy from strategic to implementation-specific choices.

Implementation Guidance: Examine task-app-python to see how documented decisions translate into consistent code patterns, architectural compliance, and maintainable software structures.

For Practical Application

TDR Creation: Use TaskFlow examples as templates for documenting technical decisions in your projects, adapting the hierarchy and content structure to your specific domain and requirements.

AI Collaboration: Apply the AI context patterns demonstrated in TaskFlow TDRs to enable effective collaboration between human decision-makers and AI development tools.

Academic Analysis Framework

Decision Methodology Evaluation

The TaskFlow examples enable systematic evaluation of DDSE methodology effectiveness through:

Decision Quality Metrics: Analysis of how explicit decision documentation affects implementation consistency, architectural compliance, and maintenance efficiency.

Traceability Assessment: Measurement of relationship clarity between strategic choices and implementation details through the documented decision hierarchy.

AI Integration Effectiveness: Evaluation of how structured decision context improves AI-generated code quality and architectural alignment.

Implementation Quality Assessment

Architectural Consistency: Comparative analysis of implementation adherence to documented architectural decisions across different system components.

Pattern Compliance: Systematic evaluation of code pattern consistency relative to documented implementation decisions and conventions.

Maintenance Predictability: Assessment of how decision documentation supports understanding and modification of existing system components.

Repository Access

Primary Repository: DDSE Foundation

Example Locations:

Supporting Resources:

The TaskFlow examples provide concrete demonstration of DDSE methodology application, enabling developers to understand both the theoretical framework and practical implementation approaches through realistic software development scenarios.