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imfozilbek b0f1778f3a docs(guardian): add research citations for 15 roadmap features 
Add comprehensive research citations for upcoming features:
- Domain Event Usage Validation (Section 15)
- Value Object Immutability (Section 16)
- CQS/CQRS (Section 17)
- Factory Pattern (Section 18)
- Specification Pattern (Section 19)
- Bounded Context (Section 20)
- Persistence Ignorance (Section 21)
- Null Object Pattern (Section 22)
- Primitive Obsession (Section 23)
- Service Locator Anti-pattern (Section 24)
- Double Dispatch/Visitor Pattern (Section 25)
- Entity Identity (Section 26)
- Saga Pattern (Section 27)
- Anti-Corruption Layer (Section 28)
- Ubiquitous Language (Section 29)

Sources include: GoF Design Patterns, Bertrand Meyer, Eric Evans,
Vaughn Vernon, Martin Fowler, Chris Richardson, Mark Seemann,
and academic papers (Garcia-Molina Sagas 1987).

Document version: 1.1 → 2.0
2025-12-04 19:11:54 +05:00

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Research Citations for Code Quality Detection Rules

This document provides authoritative sources, academic papers, industry standards, and expert references that support the code quality detection rules implemented in Guardian. These rules are not invented but based on established software engineering principles and best practices.

Table of Contents

  1. Hardcode Detection (Magic Numbers & Strings)
  2. Circular Dependencies
  3. Clean Architecture / Layered Architecture
  4. Framework Leak Detection
  5. Entity Exposure (DTO Pattern)
  6. Repository Pattern
  7. Naming Conventions
  8. General Software Quality Standards
  9. Code Complexity Metrics
  10. Additional Authoritative Sources
  11. Anemic Domain Model Detection
  12. Aggregate Boundary Validation (DDD Tactical Patterns)
  13. Secret Detection & Security
  14. Severity-Based Prioritization & Technical Debt
  15. Domain Event Usage Validation
  16. Value Object Immutability
  17. Command Query Separation (CQS/CQRS)
  18. Factory Pattern
  19. Specification Pattern
  20. Bounded Context
  21. Persistence Ignorance
  22. Null Object Pattern
  23. Primitive Obsession
  24. Service Locator Anti-pattern
  25. Double Dispatch and Visitor Pattern
  26. Entity Identity
  27. Saga Pattern
  28. Anti-Corruption Layer
  29. Ubiquitous Language

1. Hardcode Detection (Magic Numbers & Strings)

Academic Research

What do developers consider magic literals? A smalltalk perspective (2022)

  • Published in ScienceDirect
  • Conducted qualitative and quantitative studies on magic literals
  • Analyzed 26 developers reviewing about 24,000 literals from more than 3,500 methods
  • Studies ranged from small (four classes) to large (7,700 classes) systems
  • Reference: ScienceDirect Article

Industry Standards

MIT Course 6.031: Software Construction - Code Review

  • Magic numbers fail three key measures of code quality:
    • Not safe from bugs (SFB)
    • Not easy to understand (ETU)
    • Not ready for change (RFC)
  • Reference: MIT Reading 4: Code Review

SonarQube Static Analysis Rules

  • Rule RSPEC-109: "Magic numbers should not be used"
  • Identifies hardcoded values and magic numbers as code smells
  • Reference: SonarSource C Rule RSPEC-109

Historical Context

Wikipedia: Magic Number (Programming)

  • Anti-pattern that breaks one of the oldest rules of programming
  • Dating back to COBOL, FORTRAN, and PL/1 manuals of the 1960s
  • Defined as "using a numeric literal in source code that has a special meaning that is less than clear"
  • Reference: Wikipedia - Magic Number

Best Practices

DRY Principle Violation

  • Magic numbers violate the DRY (Don't Repeat Yourself) principle
  • Encourage duplicated hardcoded values instead of centralized definitions
  • Make code brittle and prone to errors
  • Reference: Stack Overflow - What are magic numbers

2. Circular Dependencies

Expert Opinion

Martin Fowler on Breaking Cycles

Impact on Software Quality

Maintainability Issues

  • Circular dependencies make code difficult to read and maintain over time
  • Open the door to error-prone applications that are difficult to test
  • Changes to a single module cause a large ripple effect of errors
  • Reference: TechTarget - Circular Dependencies

Component Coupling

Solution Patterns

Shopify Engineering: Repository Pattern

  • "Remove Circular Dependencies by Using Dependency Injection and the Repository Pattern in Ruby"
  • Demonstrates practical application of breaking circular dependencies
  • Reference: Shopify Engineering

3. Clean Architecture / Layered Architecture

The Dependency Rule - Robert C. Martin

Book: Clean Architecture: A Craftsman's Guide to Software Structure and Design (2017)

  • Author: Robert C. Martin (Uncle Bob)
  • Publisher: Prentice Hall
  • ISBN: 978-0134494166
  • Available at: Amazon

The Dependency Rule (Core Principle)

  • "Source code dependencies can only point inwards"
  • "Nothing in an inner circle can know anything at all about something in an outer circle"
  • "The name of something declared in an outer circle must not be mentioned by the code in the inner circle"
  • Reference: The Clean Architecture Blog Post

Layer Organization

  • Dependencies flow towards higher-level policies and domain logic
  • Inner layers (domain) should not depend on outer layers (infrastructure)
  • Use dynamic polymorphism to create source code dependencies that oppose the flow of control
  • Reference: Clean Architecture Beginner's Guide

O'Reilly Resources

SOLID Principles - Robert C. Martin

Paper: Design Principles and Design Patterns (2000)

  • Author: Robert C. Martin
  • Introduced the basic principles of SOLID design
  • SOLID acronym coined by Michael Feathers around 2004
  • Reference: Wikipedia - SOLID

Dependency Inversion Principle (DIP)

  • High-level modules should not depend on low-level modules; both should depend on abstractions
  • Abstractions should not depend on details; details should depend on abstractions
  • Enables loosely coupled components and simpler testing
  • Reference: DigitalOcean - SOLID Principles

Single Responsibility Principle (SRP)

  • "There should never be more than one reason for a class to change"
  • Every class should have only one responsibility
  • Classes with single responsibility are easier to understand, test, and modify
  • Reference: Real Python - SOLID Principles

4. Framework Leak Detection

Hexagonal Architecture (Ports & Adapters)

Original Paper: The Hexagonal (Ports & Adapters) Architecture (2005)

  • Author: Alistair Cockburn
  • Document: HaT Technical Report 2005.02
  • Date: 2005-09-04 (v 0.9)
  • Intent: "Allow an application to equally be driven by users, programs, automated test or batch scripts, and to be developed and tested in isolation from its eventual run-time devices and databases"
  • Reference: Alistair Cockburn - Hexagonal Architecture

Domain-Driven Design (DDD) and Hexagonal Architecture

Domain-Driven Hexagon Repository

  • Comprehensive guide combining DDD with hexagonal architecture
  • "Application Core shouldn't depend on frameworks or access external resources directly"
  • "External calls should be done through ports (interfaces)"
  • Reference: GitHub - Domain-Driven Hexagon

AWS Prescriptive Guidance

  • "The hexagonal architecture pattern is used to isolate business logic (domain logic) from related infrastructure code"
  • Outer layers can depend on inner layers, but inner layers never depend on outer layers
  • Reference: AWS - Hexagonal Architecture Pattern

Preventing Logic Leakage

Ports and Adapters Benefits

  • Shields domain logic from leaking out of application's core
  • Prevents technical details (like JPA entities) and libraries (like O/R mappers) from leaking into application
  • Keeps application agnostic of external actors
  • Reference: Medium - Hexagonal Architecture

Herberto Graca's Explicit Architecture

  • "DDD, Hexagonal, Onion, Clean, CQRS, … How I put it all together"
  • Comprehensive guide on preventing architectural leakage
  • Reference: Herberto Graca's Blog

5. Entity Exposure (DTO Pattern)

Martin Fowler's Pattern Definition

Book: Patterns of Enterprise Application Architecture (2002)

DTO Pattern Purpose

  • "The main reason for using a Data Transfer Object is to batch up what would be multiple remote calls into a single call"
  • "DTOs are called Data Transfer Objects because their whole purpose is to shift data in expensive remote calls"
  • Part of implementing a coarse-grained interface needed for remote performance
  • Reference: Martin Fowler's EAA Catalog

LocalDTO Anti-Pattern

Martin Fowler on Local DTOs

  • "In a local context, DTOs are not just unnecessary but actually harmful"
  • Harmful because coarse-grained API is more difficult to use
  • Requires extra work moving data from domain/data source layer into DTOs
  • Reference: Martin Fowler - LocalDTO

Security and Encapsulation Benefits

Baeldung: The DTO Pattern

  • DTOs provide only relevant information to the client
  • Hide sensitive data like passwords for security reasons
  • Decoupling persistence model from domain model reduces risk of exposing domain model
  • Reference: Baeldung - DTO Pattern

Wikipedia: Data Transfer Object

6. Repository Pattern

Martin Fowler's Pattern Definition

Book: Patterns of Enterprise Application Architecture (2002)

  • Author: Martin Fowler
  • Publisher: Addison-Wesley
  • ISBN: 978-0321127426
  • Available at: Internet Archive

Repository Pattern Definition

  • "Mediates between the domain and data mapping layers using a collection-like interface for accessing domain objects"
  • Listed under Data Source Architectural Patterns
  • Main goal: separate domain logic from data persistence logic
  • Reference: Martin Fowler - Repository

Pattern Purpose

  • "Adding this layer helps minimize duplicate query logic"
  • Original definition: "all about minimizing duplicate query logic"
  • Chapter 13 of online ebook at O'Reilly
  • Reference: Martin Fowler's EAA Catalog

Microsoft Guidance

Microsoft Learn: Infrastructure Persistence Layer Design

  • "Designing the infrastructure persistence layer" for microservices and DDD
  • Official Microsoft documentation on repository pattern usage
  • Reference: Microsoft Learn - Repository Pattern

Domain-Driven Design Context

Eric Evans Reference

7. Naming Conventions

Use Case Naming

Use Case Naming Convention: Verb + Noun

  • Default naming pattern: "(Actor) Verb Noun" with actor being optional
  • Name must be in the form of VERB-OBJECT with verb in imperative mode
  • Examples: "Customer Process Order", "Send Notification"
  • Reference: TM Forum - Use Case Naming Conventions

Good Use Case Names

Industry Style Guides

Google Java Style Guide

  • Method names are written in lowerCamelCase
  • Class names should be in PascalCase
  • Class names are typically nouns or noun phrases (e.g., Character, ImmutableList)
  • Reference: Google Java Style Guide

Airbnb JavaScript Style Guide

  • Avoid single letter names; be descriptive with naming
  • Use camelCase when naming objects, functions, and instances
  • Use PascalCase when exporting constructor/class/singleton
  • Filename should be identical to function's name
  • Reference: Airbnb JavaScript Style Guide

Microsoft Naming Conventions

  • Variables, methods, instance fields: camelCase
  • Class and interface names: PascalCase (capitalized CamelCase)
  • Constants: CONSTANT_CASE (all uppercase with underscores)
  • Reference: GeeksforGeeks - Java Naming Conventions

General Naming Patterns

Wikipedia: Naming Conventions

  • Classes are nouns or noun phrases
  • Methods/functions are verbs or verb phrases to identify actions
  • Established convention across multiple programming languages
  • Reference: Wikipedia - Naming Convention

Devopedia: Naming Conventions

  • Comprehensive coverage of naming conventions across languages
  • Historical context and evolution of naming standards
  • Reference: Devopedia - Naming Conventions

8. General Software Quality Standards

ISO/IEC 25010 Software Quality Model

ISO/IEC 25010:2011 (Updated 2023)

  • Title: "Systems and software engineering – Systems and software Quality Requirements and Evaluation (SQuaRE) – System and software quality models"
  • Defines eight software quality characteristics
  • Reference: ISO 25010 Official Standard

Eight Quality Characteristics

  1. Functional suitability
  2. Performance efficiency
  3. Compatibility
  4. Usability
  5. Reliability
  6. Security
  7. Maintainability
  8. Portability

Maintainability Sub-characteristics

  • Modularity: Components can be changed with minimal impact on other components
  • Reusability: Assets can be used in more than one system
  • Analysability: Effectiveness of impact assessment and failure diagnosis
  • Modifiability: System can be modified without introducing defects
  • Testability: Test criteria effectiveness and execution
  • Reference: ISO 25000 Portal

Practical Application

  • Used throughout software development lifecycle
  • Define quality requirements and evaluate products
  • Static analysis plays key role in security and maintainability
  • Reference: Perforce - What is ISO 25010

SQuaRE Framework

ISO/IEC 25000 Series

9. Code Complexity Metrics

Cyclomatic Complexity

Original Work: Thomas McCabe (1976)

  • Developed by Thomas McCabe in 1976
  • Derived from graph theory
  • Measures "the amount of decision logic in a source code function"
  • Quantifies the number of independent paths through program's source code
  • Reference: Wikipedia - Cyclomatic Complexity

NIST Recommendations

  • NIST235 indicates that a limit of 10 is a good starting point
  • Original limit of 10 proposed by McCabe has significant supporting evidence
  • Limits as high as 15 have been used successfully
  • Reference: Microsoft Learn - Cyclomatic Complexity

Research Findings

  • Positive correlation between cyclomatic complexity and defects
  • Functions with highest complexity tend to contain the most defects
  • "The SATC has found the most effective evaluation is a combination of size and (Cyclomatic) complexity"
  • Modules with both high complexity and large size have lowest reliability
  • Reference: Wikipedia - Cyclomatic Complexity

Cognitive Complexity - SonarQube

Cognitive Complexity Definition

  • Measure of how hard it is to understand code's control flow
  • Code with high cognitive complexity is hard to read, understand, test, and modify
  • Incremented when code breaks normal linear reading flow
  • Reference: SonarSource - Cognitive Complexity

Recommended Thresholds

Calculation Method

  • Counts if/else conditions, nested loops (for, forEach, do/while)
  • Includes try/catch blocks and switch statements
  • Mixed operators in conditions increase complexity
  • Reference: SonarQube Documentation - Metrics Definition

Academic Research on Software Maintainability

Tool-Based Perspective on Software Code Maintainability Metrics (2020)

Code Reviews and Complexity (2024)

  • Paper: "The utility of complexity metrics during code reviews for CSE software projects"
  • Published in: ScienceDirect
  • Analyzes metrics gathered via GitHub Actions for pull requests
  • Techniques to guide code review considering cyclomatic complexity levels
  • Reference: ScienceDirect - Complexity Metrics

10. Additional Authoritative Sources

Code Smells and Refactoring

Book: Refactoring: Improving the Design of Existing Code (1999, 2nd Edition 2018)

  • Author: Martin Fowler
  • Publisher: Addison-Wesley
  • ISBN (1st Ed): 978-0201485677
  • ISBN (2nd Ed): 978-0134757599
  • Term "code smell" first coined by Kent Beck
  • Featured in the 1999 Refactoring book
  • Reference: Martin Fowler - Code Smell

Code Smell Definition

  • "Certain structures in the code that indicate violation of fundamental design principles"
  • "Surface indication that usually corresponds to a deeper problem in the system"
  • Heuristics to indicate when to refactor
  • Reference: Wikipedia - Code Smell

Duplication as Major Code Smell

  • Duplication is one of the biggest code smells
  • Spotting duplicate code and removing it leads to improved design
  • Reference: Coding Horror - Code Smells

Domain-Driven Design

Book: Domain-Driven Design: Tackling Complexity in the Heart of Software (2003)

  • Author: Eric Evans
  • Publisher: Addison-Wesley Professional
  • ISBN: 978-0321125217
  • Available at: Amazon

DDD Reference Document

Key DDD Concepts

  • Entities: Defined by their identity
  • Value Objects: Defined by their attributes
  • Aggregates: Clusters of entities that behave as single unit
  • Repositories: Separate domain logic from persistence
  • Reference: Martin Fowler - Domain Driven Design

Code Complete - Steve McConnell

Book: Code Complete: A Practical Handbook of Software Construction (1993, 2nd Edition 2004)

  • Author: Steve McConnell
  • Publisher: Microsoft Press
  • ISBN: 978-0735619678
  • Won Jolt Award in 1993
  • Best-selling, best-reviewed software development book
  • Reference: Amazon - Code Complete

Key Topics Covered

  • Naming variables to deciding when to write a subroutine
  • Architecture, coding standards, testing, integration
  • Software craftsmanship nature
  • Main activities: detailed design, construction planning, coding, debugging, testing
  • Reference: Wikipedia - Code Complete

Architecture Testing Tools

ArchUnit - Java Architecture Testing

  • Free, simple, and extensible library for checking architecture
  • Define rules for architecture using plain Java unit tests
  • Out-of-the-box functionality for layered architecture and onion architecture
  • Enforce naming conventions, class access, prevention of cycles
  • Reference: ArchUnit Official Site

ArchUnit Examples

  • Layered Architecture Test examples on GitHub
  • Define layers and add constraints for each layer
  • Reference: GitHub - ArchUnit Examples

NetArchTest - .NET Alternative

  • Inspired by ArchUnit for Java
  • Enforce architecture conventions in .NET codebases
  • Can be used with any unit test framework
  • Reference: GitHub - NetArchTest

InfoQ Article on ArchUnit

  • "ArchUnit Verifies Architecture Rules for Java Applications"
  • Professional coverage of architecture verification
  • Reference: InfoQ - ArchUnit

11. Anemic Domain Model Detection

Martin Fowler's Original Blog Post (2003)

Blog Post: "Anemic Domain Model" (November 25, 2003)

  • Author: Martin Fowler
  • Published: November 25, 2003
  • Described as an anti-pattern related to domain driven design and application architecture
  • Basic symptom: domain objects have hardly any behavior, making them little more than bags of getters and setters
  • Reference: Martin Fowler - Anemic Domain Model

Key Problems Identified:

  • "The basic symptom of an Anemic Domain Model is that at first blush it looks like the real thing"
  • "There are objects, many named after the nouns in the domain space, and these objects are connected with the rich relationships and structure that true domain models have"
  • "The catch comes when you look at the behavior, and you realize that there is hardly any behavior on these objects"
  • "This is contrary to the basic idea of object-oriented design; which is to combine data and process together"

Why It's an Anti-pattern:

  • Fowler argues that anemic domain models incur all of the costs of a domain model, without yielding any of the benefits
  • The logic that should be in a domain object is domain logic - validations, calculations, business rules
  • Separating data from behavior violates core OOP principles
  • Reference: Wikipedia - Anemic Domain Model

Rich Domain Model vs Transaction Script

Martin Fowler: Transaction Script Pattern

  • Transaction Script organizes business logic by procedures where each procedure handles a single request
  • Good for simple logic with not-null checks and basic calculations
  • Reference: Martin Fowler - Transaction Script

When to Use Rich Domain Model:

  • If you have complicated and everchanging business rules involving validation, calculations, and derivations
  • Object model handles complex domain logic better than procedural scripts
  • Reference: InformIT - Domain Logic Patterns

Comparison:

  • Transaction Script is better for simple logic
  • Domain Model is better when things get complicated with complex business rules
  • You can refactor from Transaction Script to Domain Model, but it's a harder change
  • Reference: Medium - Transaction Script vs Domain Model

Domain-Driven Design Context

Eric Evans: Domain-Driven Design (2003)

  • Entities should have both identity and behavior
  • Rich domain models place business logic within domain entities
  • Anemic models violate DDD principles by separating data from behavior
  • Reference: Already covered in Section 10 - Domain-Driven Design Book

Community Discussion:

12. Aggregate Boundary Validation (DDD Tactical Patterns)

Eric Evans: Domain-Driven Design (2003)

Original Book Definition:

  • Aggregate: "A cluster of associated objects that we treat as a unit for the purpose of data changes"
  • An aggregate defines a consistency boundary around one or more entities
  • Exactly one entity in an aggregate is the root
  • Reference: Microsoft Learn - Tactical DDD

DDD Reference Document (2015)

Vaughn Vernon: Implementing Domain-Driven Design (2013)

Chapter 10: Aggregates (Page 347)

Key Rules from the Chapter:

  • Rule: Model True Invariants in Consistency Boundaries
  • Rule: Design Small Aggregates
  • Rule: Reference Other Aggregates by Identity
  • Rule: Use Eventual Consistency Outside the Boundary

Effective Aggregate Design Series:

  • Three-part essay series by Vaughn Vernon
  • Available as downloadable PDFs
  • Licensed under Creative Commons Attribution-NoDerivs 3.0
  • Reference: Kalele - Effective Aggregate Design

Appendix A: Aggregates and Event Sourcing:

  • Additional coverage of aggregate patterns
  • Practical implementation guidance
  • Reference: Available in the book

Tactical DDD Patterns

Microsoft Azure Architecture Center:

  • "Using tactical DDD to design microservices"
  • Official Microsoft documentation on aggregate boundaries
  • Comprehensive guide for microservices architecture
  • Reference: Microsoft Learn - Tactical DDD

SOCADK Design Practice Repository:

  • Summaries of artifacts, templates, and techniques for tactical DDD
  • Practical examples of aggregate boundary enforcement
  • Reference: SOCADK - Tactical DDD

Why Aggregate Boundaries Matter

Transactional Boundary:

  • What makes it an aggregate is the transactional boundary
  • Changes to aggregate must be atomic
  • Ensures consistency within the boundary
  • Reference: Medium - Mastering Aggregate Design

Cross-Aggregate References:

  • Aggregates should only reference other aggregates by ID, not direct entity references
  • Prevents tight coupling between aggregates
  • Maintains clear boundaries
  • Reference: Lev Gorodinski - Two Sides of DDD

13. Secret Detection & Security

OWASP Standards

OWASP Secrets Management Cheat Sheet

  • Official OWASP best practices and guidelines for secrets management
  • Comprehensive coverage of hardcoded credentials risks
  • Reference: OWASP - Secrets Management

OWASP DevSecOps Guideline

OWASP Password Management: Hardcoded Password

Key Security Principles

Don't Hardcode Secrets:

Centralized Management:

  • Growing need to centralize storage, provisioning, auditing, rotation, and management of secrets
  • Control access and prevent secrets from leaking
  • Use purpose-built tools for encryption-at-rest
  • Reference: OWASP SAMM - Secret Management

Prevention Tools:

GitHub Secret Scanning

Official GitHub Documentation:

  • About Secret Scanning: Automated detection of secrets in repositories
  • Scans for patterns and heuristics matching known types of secrets
  • Reference: GitHub Docs - Secret Scanning

How It Works:

  • Automatically scans repository contents for sensitive data (API keys, passwords, tokens)
  • Scans commits, issues, and pull requests continuously
  • Real-time alerts to repository administrators
  • Reference: GitHub Docs - Keeping Secrets Secure

AI-Powered Detection:

  • Copilot Secret Scanning uses large language models (LLMs)
  • Identifies unstructured secrets (generic passwords) in source code
  • Enhances detection beyond pattern matching
  • Reference: GitHub Docs - Copilot Secret Scanning

Supported Patterns:

Mobile Security

OWASP Mobile Security:

  • "Secrets security is the most important issue for mobile applications"
  • Only safe way: keep secrets off the client side entirely
  • Move sensitive information to backend
  • Reference: GitGuardian - OWASP Top 10 Mobile

Third-Party Tools

GitGuardian:

Yelp detect-secrets:

14. Severity-Based Prioritization & Technical Debt

Academic Research on Technical Debt Prioritization

Systematic Literature Review (2020)

  • Title: "A systematic literature review on Technical Debt prioritization"
  • Analyzed 557 unique papers, included 44 primary studies
  • Finding: "Technical Debt prioritization research is preliminary and there is no consensus on what the important factors are and how to measure them"
  • Reference: ScienceDirect - TD Prioritization

IEEE Conference Paper (2021)

  • Title: "Technical Debt Prioritization: Taxonomy, Methods Results, and Practical Characteristics"
  • Systematic mapping review of 112 studies, resulting in 51 unique papers
  • Classified methods in two-level taxonomy with 10 categories
  • Reference: IEEE Xplore - TD Prioritization

Identifying Severity of Technical Debt (2023)

  • Journal: Software Quality Journal
  • Title: "Identifying the severity of technical debt issues based on semantic and structural information"
  • Problem: "Existing studies mainly focus on detecting TD through source code or comments but usually ignore the severity degree of TD issues"
  • Proposed approach combining semantic and structural information
  • Reference: Springer - TD Severity

SonarQube Severity Classification

Current Severity Levels (SonarQube 10.2+)

High/Blocker Severity:

  • An issue with significant probability of severe unintended consequences
  • Should be fixed immediately
  • Includes bugs leading to production crashes
  • Security flaws allowing attackers to extract sensitive data or execute malicious code
  • Reference: SonarQube Docs - Metrics

Medium Severity:

  • Quality flaw that can highly impact developer's productivity
  • Uncovered code, duplicated blocks, unused parameters
  • Reference: SonarQube Documentation

Low Severity:

  • Quality flaw with slight impact on developer productivity
  • Lines too long, switch statements with few cases
  • Reference: SonarQube Documentation

Info Severity:

Legacy SonarQube Classification (pre-10.2)

Five Severity Levels:

  • BLOCKER: Bug with high probability to impact behavior in production (memory leak, unclosed JDBC connection)
  • CRITICAL: Bug with low probability to impact production behavior OR security flaw (empty catch block, SQL injection)
  • MAJOR: Quality flaw highly impacting developer productivity (uncovered code, duplicated blocks, unused parameters)
  • MINOR: Quality flaw slightly impacting developer productivity (lines too long, switch statements < 3 cases)
  • INFO: Informational only
  • Reference: SonarQube Community - Severity Categories

Research on Impact and Effectiveness

Empirical Study (2020)

  • Title: "Some SonarQube issues have a significant but small effect on faults and changes"
  • Published in: ScienceDirect (Information and Software Technology)
  • Large-scale empirical study on SonarQube issue impact
  • Reference: ScienceDirect - SonarQube Issues

Machine Learning for Prioritization (2024)

  • Recent approaches: "Development teams could integrate models into CI/CD pipelines"
  • Automatically flag potential TD issues during code reviews
  • Prioritize based on severity
  • Reference: arXiv - Technical Debt Management

Multiple-Case Study

Aligning TD with Business Objectives (2018)

  • Title: "Aligning Technical Debt Prioritization with Business Objectives: A Multiple-Case Study"
  • Demonstrates importance of priority-based technical debt management
  • Reference: ResearchGate - TD Business Alignment

15. Domain Event Usage Validation

Eric Evans: Domain-Driven Design (2003)

Original Definition:

  • Domain Events: "Something happened that domain experts care about"
  • Events capture facts about the domain that have already occurred
  • Distinct from system events - they model business-relevant occurrences
  • Reference: Martin Fowler - Domain Event

Book: Domain-Driven Design (2003)

  • Author: Eric Evans
  • Publisher: Addison-Wesley Professional
  • ISBN: 978-0321125217
  • Domain Events weren't explicitly in the original book but evolved from DDD community
  • Reference: DDD Community - Domain Events

Vaughn Vernon: Implementing Domain-Driven Design (2013)

Chapter 8: Domain Events

  • Author: Vaughn Vernon
  • Comprehensive coverage of Domain Events implementation
  • "Model information about activity in the domain as a series of discrete events"
  • Reference: Amazon - Implementing DDD

Key Principles:

  • Events should be immutable
  • Named in past tense (OrderPlaced, UserRegistered)
  • Contain all data needed by handlers
  • Enable loose coupling between aggregates

Martin Fowler's Event Patterns

Event Sourcing:

  • "Capture all changes to an application state as a sequence of events"
  • Events become the primary source of truth
  • Reference: Martin Fowler - Event Sourcing

Event-Driven Architecture:

Why Direct Infrastructure Calls Are Bad

Coupling Issues:

  • Direct calls create tight coupling between domain and infrastructure
  • Makes testing difficult (need to mock infrastructure)
  • Violates Single Responsibility Principle
  • Reference: Microsoft - Domain Events Design

Benefits of Domain Events:

  • Decouples domain from side effects
  • Enables eventual consistency
  • Improves testability
  • Supports audit logging naturally
  • Reference: Jimmy Bogard - Domain Events

16. Value Object Immutability

Eric Evans: Domain-Driven Design (2003)

Value Object Definition:

  • "An object that describes some characteristic or attribute but carries no concept of identity"
  • "Value Objects should be immutable"
  • When you care only about the attributes of an element, classify it as a Value Object
  • Reference: Martin Fowler - Value Object

Immutability Requirement:

  • "Treat the Value Object as immutable"
  • "Don't give it any identity and avoid the design complexities necessary to maintain Entities"
  • Reference: DDD Reference - Value Objects

Martin Fowler on Value Objects

Blog Post: Value Object (2016)

  • "A small simple object, like money or a date range, whose equality isn't based on identity"
  • "I consider value objects to be one of the most important building blocks of good domain models"
  • Reference: Martin Fowler - Value Object

Key Properties:

  • Equality based on attribute values, not identity
  • Should be immutable (once created, cannot be changed)
  • Side-effect free behavior
  • Self-validating (validate in constructor)

Vaughn Vernon: Implementing DDD

Chapter 6: Value Objects

  • Detailed implementation guidance
  • "Measures, quantifies, or describes a thing in the domain"
  • "Can be compared with other Value Objects using value equality"
  • "Completely replaceable when the measurement changes"
  • Reference: Vaughn Vernon - Implementing DDD

Why Immutability Matters

Thread Safety:

  • Immutable objects are inherently thread-safe
  • No synchronization needed for concurrent access
  • Reference: Effective Java - Item 17

Reasoning About Code:

Functional Programming Influence:

  • Immutability is a core principle of functional programming
  • Reduces side effects and makes code more predictable
  • Reference: Wikipedia - Immutable Object

17. Command Query Separation (CQS/CQRS)

Bertrand Meyer: Original CQS Principle

Book: Object-Oriented Software Construction (1988, 2nd Ed. 1997)

  • Author: Bertrand Meyer
  • Publisher: Prentice Hall
  • ISBN: 978-0136291558
  • Introduced Command Query Separation principle
  • Reference: Wikipedia - CQS

CQS Principle:

  • "Every method should either be a command that performs an action, or a query that returns data to the caller, but not both"
  • Commands: change state, return nothing (void)
  • Queries: return data, change nothing (side-effect free)
  • Reference: Martin Fowler - CommandQuerySeparation

Greg Young: CQRS Pattern

CQRS Documents (2010)

  • Author: Greg Young
  • Extended CQS to architectural pattern
  • "CQRS is simply the creation of two objects where there was previously only one"
  • Reference: Greg Young - CQRS Documents

Key Concepts:

  • Separate models for reading and writing
  • Write model (commands) optimized for business logic
  • Read model (queries) optimized for display/reporting
  • Reference: Microsoft - CQRS Pattern

Martin Fowler on CQRS

Blog Post: CQRS (2011)

  • "At its heart is the notion that you can use a different model to update information than the model you use to read information"
  • Warns against overuse: "CQRS is a significant mental leap for all concerned"
  • Reference: Martin Fowler - CQRS

Benefits and Trade-offs

Benefits:

  • Independent scaling of read and write workloads
  • Optimized data schemas for each side
  • Improved security (separate read/write permissions)
  • Reference: AWS - CQRS Pattern

Trade-offs:

18. Factory Pattern

Gang of Four: Design Patterns (1994)

Book: Design Patterns: Elements of Reusable Object-Oriented Software

  • Authors: Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides (Gang of Four)
  • Publisher: Addison-Wesley
  • ISBN: 978-0201633610
  • Defines Factory Method and Abstract Factory patterns
  • Reference: Wikipedia - Design Patterns

Factory Method Pattern:

  • "Define an interface for creating an object, but let subclasses decide which class to instantiate"
  • Lets a class defer instantiation to subclasses
  • Reference: Refactoring Guru - Factory Method

Abstract Factory Pattern:

Eric Evans: Factory in DDD Context

Domain-Driven Design (2003)

  • Chapter 6: "The Life Cycle of a Domain Object"
  • Factories encapsulate complex object creation
  • "Shift the responsibility for creating instances of complex objects and Aggregates to a separate object"
  • Reference: DDD Reference

DDD Factory Guidelines:

  • Factory should create valid objects (invariants satisfied)
  • Two types: Factory for new objects, Factory for reconstitution
  • Keep creation logic out of the entity itself
  • Reference: Already in Section 10 - Domain-Driven Design

Why Factories Matter in DDD

Encapsulation of Creation Logic:

Factory vs Constructor:

19. Specification Pattern

Eric Evans & Martin Fowler

Original Paper: Specifications (1997)

  • Authors: Eric Evans and Martin Fowler
  • Introduced the Specification pattern
  • "A Specification states a constraint on the state of another object"
  • Reference: Martin Fowler - Specification

Domain-Driven Design (2003)

  • Chapter 9: "Making Implicit Concepts Explicit"
  • Specifications make business rules explicit and reusable
  • "Create explicit predicate-like Value Objects for specialized purposes"
  • Reference: DDD Reference

Pattern Definition

Core Concept:

  • Specification is a predicate that determines if an object satisfies some criteria
  • Encapsulates business rules that can be reused and combined
  • Reference: Wikipedia - Specification Pattern

Three Main Uses:

  1. Selection: Finding objects that match criteria
  2. Validation: Checking if object satisfies rules
  3. Construction: Describing what needs to be created

Composite Specifications

Combining Specifications:

Benefits:

20. Bounded Context

Eric Evans: Domain-Driven Design (2003)

Original Definition:

  • "A Bounded Context delimits the applicability of a particular model"
  • "Explicitly define the context within which a model applies"
  • Chapter 14: "Maintaining Model Integrity"
  • Reference: Martin Fowler - Bounded Context

Key Principles:

  • Each Bounded Context has its own Ubiquitous Language
  • Same term can mean different things in different contexts
  • Models should not be shared across context boundaries
  • Reference: DDD Reference

Vaughn Vernon: Strategic Design

Implementing Domain-Driven Design (2013)

Context Mapping Patterns:

  • Shared Kernel
  • Customer/Supplier
  • Conformist
  • Anti-Corruption Layer
  • Open Host Service / Published Language
  • Reference: Context Mapping Patterns

Why Bounded Contexts Matter

Avoiding Big Ball of Mud:

  • Without explicit boundaries, models become entangled
  • Different teams step on each other's models
  • Reference: Wikipedia - Big Ball of Mud

Microservices and Bounded Contexts:

Cross-Context Communication

Integration Patterns:

  • Never share domain models across contexts
  • Use integration events or APIs
  • Translate between context languages
  • Reference: Microsoft - Tactical DDD

21. Persistence Ignorance

Definition and Principles

Core Concept:

  • Domain objects should have no knowledge of how they are persisted
  • Business logic remains pure and testable
  • Infrastructure concerns are separated from domain
  • Reference: Microsoft - Persistence Ignorance

Wikipedia Definition:

  • "Persistence ignorance is the ability of a class to be used without any underlying persistence mechanism"
  • Objects don't know if/how they'll be stored
  • Reference: Wikipedia - Persistence Ignorance

Eric Evans: DDD and Persistence

Domain-Driven Design (2003)

  • Repositories abstract away persistence details
  • Domain model should not reference ORM or database concepts
  • Reference: Already covered in Section 6 - Repository Pattern

Key Quote:

  • "The domain layer should be kept clean of all technical concerns"
  • ORM annotations violate this principle
  • Reference: Clean Architecture and DDD

Clean Architecture Alignment

Robert C. Martin:

  • "The database is a detail"
  • Domain entities should not depend on persistence frameworks
  • Use Repository interfaces to abstract persistence
  • Reference: Clean Architecture Book

Practical Implementation

Two-Model Approach:

Benefits:

22. Null Object Pattern

Original Pattern

Pattern Languages of Program Design 3 (1997)

Definition:

  • "A Null Object provides a 'do nothing' behavior, hiding the details from its collaborators"
  • Replaces null checks with polymorphism
  • Reference: Refactoring Guru - Null Object

Martin Fowler's Coverage

Refactoring Book (1999, 2018)

  • "Introduce Null Object" refactoring
  • "Replace conditional logic that checks for null with a null object"
  • Reference: Refactoring Catalog

Special Case Pattern:

  • More general pattern that includes Null Object
  • "A subclass that provides special behavior for particular cases"
  • Reference: Martin Fowler - Special Case

Benefits

Eliminates Null Checks:

  • Reduces cyclomatic complexity
  • Cleaner, more readable code
  • Follows "Tell, Don't Ask" principle
  • Reference: SourceMaking - Null Object

Polymorphism Over Conditionals:

  • Null Object responds to same interface as real object
  • Default/neutral behavior instead of null checks
  • Reference: C2 Wiki - Null Object

When to Use

Good Candidates:

Cautions:

23. Primitive Obsession

Code Smell Definition

Martin Fowler: Refactoring (1999, 2018)

  • Primitive Obsession is a code smell
  • "Using primitives instead of small objects for simple tasks"
  • Reference: Refactoring Catalog

Wikipedia Definition:

  • "Using primitive data types to represent domain ideas"
  • Example: Using string for email, int for money
  • Reference: Wikipedia - Code Smell

Why It's a Problem

Lost Type Safety:

Scattered Validation:

Missing Behavior:

Solutions

Replace with Value Objects:

  • Money instead of decimal
  • Email instead of string
  • PhoneNumber instead of string
  • Reference: Already covered in Section 16 - Value Object Immutability

Replace Data Value with Object:

Common Primitive Obsession Examples

Frequently Misused Primitives:

  • string for: email, phone, URL, currency code, country code
  • int/decimal for: money, percentage, age, quantity
  • DateTime for: date ranges, business dates
  • Reference: DDD - Value Objects

24. Service Locator Anti-pattern

Martin Fowler's Analysis

Blog Post: Inversion of Control Containers and the Dependency Injection pattern (2004)

  • Compares Service Locator with Dependency Injection
  • "With service locator the application class asks for it explicitly by a message to the locator"
  • Reference: Martin Fowler - Inversion of Control

Service Locator Definition:

  • "The basic idea behind a service locator is to have an object that knows how to get hold of all of the services that an application might need"
  • Acts as a registry that provides dependencies on demand
  • Reference: Martin Fowler - Service Locator

Why It's Considered an Anti-pattern

Mark Seemann: Dependency Injection in .NET (2011, 2nd Ed. 2019)

Hidden Dependencies:

  • Dependencies are not visible in constructor
  • Makes code harder to understand and test
  • Violates Explicit Dependencies Principle
  • Reference: DevIQ - Explicit Dependencies

Testing Difficulties:

Dependency Injection Alternative

Constructor Injection:

  • Dependencies declared in constructor
  • Compiler enforces dependency provision
  • Clear, testable code
  • Reference: Already covered in Section 6 - Repository Pattern

Benefits over Service Locator:

25. Double Dispatch and Visitor Pattern

Gang of Four: Visitor Pattern

Design Patterns (1994)

  • Authors: Gang of Four
  • Visitor Pattern chapter
  • "Represent an operation to be performed on the elements of an object structure"
  • Reference: Wikipedia - Visitor Pattern

Intent:

  • "Lets you define a new operation without changing the classes of the elements on which it operates"
  • Separates algorithms from object structure
  • Reference: Refactoring Guru - Visitor

Double Dispatch Mechanism

Definition:

  • "A mechanism that dispatches a function call to different concrete functions depending on the runtime types of two objects involved in the call"
  • Visitor pattern uses double dispatch
  • Reference: Wikipedia - Double Dispatch

How It Works:

  1. Client calls element.accept(visitor)
  2. Element calls visitor.visit(this) - first dispatch
  3. Correct visit() overload selected - second dispatch

When to Use

Good Use Cases:

  • Operations on complex object structures
  • Many distinct operations needed
  • Object structure rarely changes but operations change often
  • Reference: Refactoring Guru - Visitor Use Cases

Alternative to Type Checking:

Trade-offs

Advantages:

  • Open/Closed Principle for new operations
  • Related operations grouped in one class
  • Accumulate state while traversing
  • Reference: GoF Design Patterns

Disadvantages:

  • Adding new element types requires changing all visitors
  • May break encapsulation (visitors need access to element internals)
  • Reference: C2 Wiki - Visitor Pattern

26. Entity Identity

Eric Evans: Domain-Driven Design (2003)

Entity Definition:

  • "An object that is not defined by its attributes, but rather by a thread of continuity and its identity"
  • "Some objects are not defined primarily by their attributes. They represent a thread of identity"
  • Reference: Martin Fowler - Evans Classification

Identity Characteristics:

  • Unique within the system
  • Stable over time (doesn't change)
  • Survives state changes
  • Reference: DDD Reference

Vaughn Vernon: Identity Implementation

Implementing Domain-Driven Design (2013)

  • Chapter 5: "Entities"
  • Detailed coverage of identity strategies
  • "The primary characteristic of an Entity is that it has a unique identity"
  • Reference: Vaughn Vernon - Implementing DDD

Identity Types:

  • Natural keys (SSN, email)
  • Surrogate keys (UUID, auto-increment)
  • Domain-generated IDs
  • Reference: Microsoft - Entity Keys

Identity Best Practices

Immutability of Identity:

Value Object for Identity:

Equality Based on Identity:

27. Saga Pattern

Original Research

Paper: Sagas (1987)

  • Authors: Hector Garcia-Molina and Kenneth Salem
  • Published: ACM SIGMOD Conference
  • Introduced Sagas for long-lived transactions
  • Reference: ACM Digital Library - Sagas

Definition:

  • "A saga is a sequence of local transactions where each transaction updates data within a single service"
  • Alternative to distributed transactions
  • Reference: Microsoft - Saga Pattern

Chris Richardson: Microservices Patterns

Book: Microservices Patterns (2018)

Saga Types:

  1. Choreography: Each service publishes events that trigger next steps
  2. Orchestration: Central coordinator tells services what to do

Compensating Transactions

Core Concept:

  • Each step has a compensating action to undo it
  • If step N fails, compensate steps N-1, N-2, ..., 1
  • Reference: AWS - Saga Pattern

Compensation Examples:

Trade-offs

Advantages:

  • Works across service boundaries
  • No distributed locks
  • Services remain autonomous
  • Reference: Chris Richardson - Saga

Challenges:

28. Anti-Corruption Layer

Eric Evans: Domain-Driven Design (2003)

Original Definition:

  • Chapter 14: "Maintaining Model Integrity"
  • "Create an isolating layer to provide clients with functionality in terms of their own domain model"
  • Protects your model from external/legacy models
  • Reference: DDD Reference

Purpose:

Microsoft Guidance

Azure Architecture Center:

  • "Implement a facade or adapter layer between different subsystems that don't share the same semantics"
  • Isolate subsystems by placing an anti-corruption layer between them
  • Reference: Microsoft - ACL Pattern

When to Use:

  • Integrating with legacy systems
  • Migrating from monolith to microservices
  • Working with third-party APIs
  • Reference: Microsoft - ACL When to Use

Components of ACL

Facade:

Adapter:

Translator:

Benefits

Isolation:

Gradual Migration:

29. Ubiquitous Language

Eric Evans: Domain-Driven Design (2003)

Original Definition:

  • Chapter 2: "Communication and the Use of Language"
  • "A language structured around the domain model and used by all team members"
  • "The vocabulary of that Ubiquitous Language includes the names of classes and prominent operations"
  • Reference: Martin Fowler - Ubiquitous Language

Key Principles:

  • Shared by developers and domain experts
  • Used in code, conversations, and documentation
  • Changes to language reflect model changes
  • Reference: DDD Reference

Why It Matters

Communication Benefits:

  • Reduces translation between business and tech
  • Catches misunderstandings early
  • Domain experts can read code names
  • Reference: InfoQ - Ubiquitous Language

Design Benefits:

Building Ubiquitous Language

Glossary:

Event Storming:

Common Pitfalls

Inconsistent Terminology:

Technical Terms in Domain:

Conclusion

The code quality detection rules implemented in Guardian are firmly grounded in:

  1. Academic Research: Peer-reviewed papers on software maintainability, complexity metrics, code quality, technical debt prioritization, severity classification, and distributed systems (Sagas)
  2. Industry Standards: ISO/IEC 25010, SonarQube rules, OWASP security guidelines, Google and Airbnb style guides
  3. Authoritative Books:
    • Gang of Four's "Design Patterns" (1994)
    • Bertrand Meyer's "Object-Oriented Software Construction" (1988, 1997)
    • Robert C. Martin's "Clean Architecture" (2017)
    • Vaughn Vernon's "Implementing Domain-Driven Design" (2013)
    • Chris Richardson's "Microservices Patterns" (2018)
    • Eric Evans' "Domain-Driven Design" (2003)
    • Martin Fowler's "Patterns of Enterprise Application Architecture" (2002)
    • Martin Fowler's "Refactoring" (1999, 2018)
    • Steve McConnell's "Code Complete" (1993, 2004)
    • Joshua Bloch's "Effective Java" (2001, 2018)
    • Mark Seemann's "Dependency Injection in .NET" (2011, 2019)
    • Bobby Woolf's "Null Object" in PLoPD3 (1997)
  4. Expert Guidance: Martin Fowler, Robert C. Martin (Uncle Bob), Eric Evans, Vaughn Vernon, Alistair Cockburn, Kent Beck, Greg Young, Bertrand Meyer, Mark Seemann, Chris Richardson, Alberto Brandolini
  5. Security Standards: OWASP Secrets Management, GitHub Secret Scanning, GitGuardian best practices
  6. Open Source Tools: ArchUnit, SonarQube, ESLint, Secretlint - widely adopted in enterprise environments
  7. DDD Tactical & Strategic Patterns: Domain Events, Value Objects, Entities, Aggregates, Bounded Contexts, Anti-Corruption Layer, Ubiquitous Language, Specifications, Factories
  8. Architectural Patterns: CQS/CQRS, Saga, Visitor/Double Dispatch, Null Object, Persistence Ignorance

These rules represent decades of software engineering wisdom, empirical research, security best practices, and battle-tested practices from the world's leading software organizations and thought leaders.

Additional Resources

Online Catalogs and References

GitHub Repositories

Educational Institutions

Document Version: 2.0 Last Updated: 2025-12-04 Questions or want to contribute research?

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