Draft:Independent Variation Principle

Submission declined on 4 December 2025 by Rambley (talk). This draft's references do not show that the subject qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are: in-depth (not just passing mentions about the subject) reliable secondary independent of the subject Make sure you add references that meet these criteria before resubmitting. Learn about mistakes to avoid when addressing this issue. If no additional references exist, the subject is not suitable for Wikipedia. Your draft shows signs of having been generated by a large language model, such as ChatGPT. Wikipedia guidelines prohibit the use of LLMs to write articles from scratch. In addition, LLM-generated articles usually have multiple quality issues, to include: Promotional tone, editorializing and other words to watch Vague, generic, and speculative statements extrapolated from similar subjects Essay-like writing Hallucinations (plausible-sounding, but false information) and non-existent references Close paraphrasing Please address these issues. The best way is usually to read reliable sources and summarize them, instead of using a large language model. See our help page on large language models. If you would like to continue working on the submission, click on the "Edit" tab at the top of the window. If you have not resolved the issues listed above, your draft will be declined again and potentially deleted. If you need extra help, please ask us a question at the AfC Help Desk or get live help from experienced editors. Please do not remove reviewer comments or this notice until the submission is accepted. Where to get help If you need help editing or submitting your draft, please ask us a question at the AfC Help Desk or get live help from experienced editors. These venues are only for help with editing and the submission process, not to get reviews. If you need feedback on your draft, or if the review is taking a lot of time, you can try asking for help on the talk page of a relevant WikiProject. Some WikiProjects are more active than others so a speedy reply is not guaranteed. How to improve a draft Wikipedia:Contributing to Wikipedia – a basic overview on how to edit Wikipedia. Help:Wikitext – how to use the markup Help:Referencing for beginners – how to include references Wikipedia:Article development – how to develop your article Wikipedia:Writing better articles – how to improve your article Wikipedia:Verifiability – make sure your article includes reliable third-party sources You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Add tags to your draft Editor resources Easy tools: Citation bot (help) | Advanced: Fix bare URLs Declined by Rambley 12 days ago. Last edited by Rambley 12 days ago. Reviewer: Inform author. Resubmit Please note that if the issues are not fixed, the draft will be declined again.

• Comment: At least from what it looks like, only one source is actually covering this topic in depth. Rambley ^{(talk / contribs)} 09:13, 4 December 2025 (UTC)

• Comment: In accordance with Wikipedia's Conflict of interest policy, I disclose that I have a conflict of interest regarding the subject of this article. Albanick (talk) 08:44, 4 December 2025 (UTC)

The Independent Variation Principle (IVP) is a meta-principle in software architecture—a principle that explains and unifies other principles—that provides a theoretical foundation for managing system evolution and maintenance costs. First introduced by Yannick Loth in 2025,^[1] IVP states that software systems should be structured such that elements varying independently are separated, while elements varying dependently are unified.

IVP has three equivalent formulations, each emphasizing different aspects:

Separate elements that vary independently; unify elements that vary dependently.

This formulation focuses on observable patterns of variation: whether elements change together or separately.

Separate what varies for different causes; unite what varies for the same cause.

This emphasizes the underlying forces driving change rather than merely observed patterns.

Separate elements governed by different change drivers into distinct units; unify elements governed by the same change driver within a single unit.

This makes explicit how to organize code: align system structure with the causal structure of change forces.

The Independent Variation Principle was introduced in Loth's 2025 paper "The Independent Variation Principle: A Unifying Meta-Principle for Software Architecture".^[1] The principle emerged from analyzing why established software design principles work and identifying their common foundation.

IVP represents an evolution of earlier work on separation of concerns,^[2] providing a formalization grounded in change drivers rather than structural properties. The principle builds on decades of software architecture research, particularly the work of Robert C. Martin on SOLID^[3] and package principles.^[4]

IVP relies on four foundational concepts:

An element is an atomic code component that can participate in dependencies and be modified in response to change. Elements include classes, functions, modules, files, or closures—the fundamental units that form nodes in a dependency graph.

A dependency between software elements represents a relationship where changes to one element may necessitate changes to another. Dependencies include method calls, imports, data flow, inheritance, and composition.

A change driver (also called a variation source or concern) is an external force that necessitates modifications to elements or their dependencies. Change drivers arise from:

• Business requirements (new features, competitive pressures)
• Regulatory compliance (legal obligations, security mandates)
• Technology evolution (framework updates, performance needs)
• Stakeholder needs (end users, administrators, auditors)

Relationship to Stressors: Barry O'Reilly's Residuality Theory^[5] uses the term stressor for events that prompt system modifications. Stressors and change drivers are complementary concepts: a stressor is a specific event that creates or modifies knowledge about the system (e.g., a server failure or regulatory change), while a change driver is the dimension along which that knowledge varies (e.g., infrastructure or compliance). Multiple stressors may affect the same change driver, and stressors can reveal previously unidentified change drivers when their effects don't map to existing variation axes.

A unit groups software elements governed by the same change driver. Units can be nested hierarchically at different granularities: classes, modules, packages, services, or Domain-Driven Design's bounded contexts.

IVP achieves its objectives through two complementary directives:

When elements vary independently, separate them into different modules.

This directive achieves low coupling by preventing unrelated changes from affecting each other. Concerns are divergent when they serve different stakeholders, change at different rates, respond to different requirement types, require different expertise, or operate at different abstraction levels.

When elements vary together, unify them in the same module.

This directive achieves high cohesion by localizing related changes. Critically, IVP defines cohesion in causal terms (elements that change for the same reason) rather than structural terms (elements that access the same data).

IVP introduces the concept of causal cohesion: a module exhibits causal cohesion when its elements vary in response to the same change driver. This contrasts with traditional structural cohesion metrics that measure observable relationships like shared data access after-the-fact.

A central contribution of IVP is the Knowledge Theorem, which establishes that cohesion is fundamentally epistemic rather than structural:

Maximal causal cohesion equals complete formalized domain knowledge.

According to this theorem, a highly cohesive module:

• Contains complete knowledge of its domain (nothing relevant is missing)
• Contains only knowledge of its domain (nothing irrelevant is included)
• Makes knowledge explicit (formalized as named code constructs, not just implicit understanding)

This transforms the understanding of software architecture from merely organizing code components to fundamentally organizing knowledge. High cohesion indicates that a module contains a complete, explicit representation of exactly one domain's knowledge.

IVP functions as a meta-principle that explains and unifies established software design principles:

The paper derives several SOLID principles from IVP:

Single Responsibility Principle (SRP): IVP generalizes SRP's insight that "a class should have only one reason to change." SRP is IVP applied at the class level, where "reason to change" corresponds to a change driver.

Open/Closed Principle (OCP): IVP reveals that abstractions remain stable when they represent the intersection of multiple change drivers' requirements, while implementations vary independently.

Dependency Inversion Principle (DIP): IVP shows that DIP works by decoupling independent change drivers (high-level policies vs. low-level details) while materializing domain knowledge through abstractions.

Interface Segregation Principle (ISP): IVP explains that "fat" interfaces couple multiple independent change drivers (various client needs), while segregated interfaces enable independent variation.

The Liskov Substitution Principle (LSP) is not explicitly derived from IVP in the original paper.

Robert Cecil Martin's Package Architecture Principles (also called Package Cohesion and Coupling Principles) emerge as applications of IVP at the package/component level:

Common Closure Principle (CCP): "Classes that change together belong together." This is a direct application of IVP-2 (unify by single purpose) at the package level. Elements that vary in response to the same change driver should be grouped in the same package.

Common Reuse Principle (CRP): "Classes that are used together belong together." IVP explains that reuse patterns indicate shared change drivers: if clients always use certain classes together, those classes likely serve the same concern and should be unified.

Reuse/Release Equivalence Principle (REP): IVP demonstrates that releasable units should contain only elements governed by the same change driver, ensuring that releases remain focused and predictable.

Acyclic Dependencies Principle (ADP): IVP shows that cycles couple independent change drivers, creating ripple effects where changes propagate unexpectedly. Breaking cycles enables independent variation.

Stable Dependencies Principle (SDP): IVP explains that dependencies should flow toward more stable modules because stable modules have fewer active change drivers, reducing the likelihood of change propagation.

Stable Abstractions Principle (SAP): IVP reveals that abstractions should be stable because they represent the intersection of multiple change drivers' requirements, making them naturally resistant to change.

DRY (Don't Repeat Yourself): Duplication couples change drivers because the same change is required in multiple places, violating IVP-2 (unify by single purpose).

Separation of Concerns (SoC): IVP reformulates SoC with actionable directives and explicit focus on variation independence.

Information Hiding: IVP shows that information hiding enables independent variation by preventing implementation details from coupling to client code.

Law of Demeter: IVP demonstrates that minimizing knowledge of other objects' structures reduces coupling between independent change drivers.

The original paper demonstrates IVP's applicability through several design decision analyses:

Analysis of invoice processing systems shows that introducing domain-owned abstractions decouples business rules from infrastructure technology, enabling independent variation. The progression from concrete dependencies to abstraction-based designs demonstrates measurable improvements in coupling strength and cohesion.

IVP analysis reveals that immutable data structures decouple two independent change drivers: state evolution (what changes) and concurrent access (how many components access data). Immutability achieves this by eliminating the need for synchronization in transformation logic.

For linear sequences, iteration decouples algorithm logic from execution control (stack depth limits), enabling independent variation. For hierarchical structures, recursion may provide superior clarity despite coupling.

IVP demonstrates that composition decouples payment method variety from common infrastructure concerns, enabling each to evolve independently. Inheritance couples these concerns through base class dependencies, reducing evolvability.

IVP provides a systematic framework for analyzing and improving software designs. The original paper proposes a six-step method for applying IVP to architectural decisions:

Begin by identifying the software elements involved in the design decision. Elements are the atomic components that can participate in dependencies and be modified in response to change. Depending on the granularity of analysis, elements may be:

• Functions or methods
• Classes or types
• Modules or files
• Packages or components
• Services or bounded contexts

Determine what forces could require changes to these elements. Change drivers can be identified by:

• Stakeholder analysis: Who makes decisions about changes to each element? Different stakeholders (business analysts, database administrators, security officers) often represent different change drivers.
• Historical analysis: Examine version control history and issue tracking to identify patterns of change.
• Concern categorization: Distinguish between business concerns (domain rules, workflows), technical concerns (performance, security, persistence), and cross-cutting concerns (logging, monitoring, error handling).

Common change drivers include:

• Business requirements and domain logic
• User interface design and user experience
• Data persistence and database technology
• External API integration
• Performance optimization
• Security and compliance requirements
• Deployment and infrastructure

Evaluate whether the identified change drivers are independent or dependent:

• Independent drivers arise from different concerns, could change without affecting each other, involve different stakeholders, and operate at different rates or timescales.
• Dependent drivers consistently change together, serve the same stakeholder group, arise from the same concern, and require coordinated modifications.

Ask: "If change driver A requires modifications, must change driver B also change?" If the answer is "not necessarily," the drivers are independent.

Based on the independence assessment, apply the appropriate directive:

• For independent drivers: Apply IVP-1 (Isolate Divergent Concerns). Separate elements governed by different change drivers into distinct modules, packages, or layers. Introduce abstractions to decouple them if they must interact.
• For dependent drivers: Apply IVP-2 (Unify by Single Purpose). Group elements governed by the same change driver into the same module or package. Ensure the module contains complete knowledge of the concern.

Compare alternative designs by analyzing:

• Coupling strength: Count and categorize dependencies between modules. Fewer dependencies crossing change driver boundaries indicate better designs.
• Cohesion quality: Assess whether modules contain complete, focused knowledge of single concerns. Modules should contain all knowledge relevant to one concern and no knowledge relevant to other concerns.
• Change propagation: Trace how hypothetical changes would propagate through the system. Better designs localize changes to single modules.
• Knowledge completeness: Verify that all domain concepts, relationships and rules are explicitly represented in code, not just implicitly understood.

Choose the design that maximizes independent variation for the active change drivers in your specific context. Consider:

• Context-specific priorities: Some contexts prioritize performance over evolvability, security over flexibility, or simplicity over extensibility.
• Incremental refinement: IVP compliance can be achieved gradually through refactoring rather than requiring complete system redesigns.
• Pragmatic tradeoffs: Perfect IVP compliance may conflict with other legitimate concerns (performance, deployment constraints, team boundaries).

When applying this method, consider these guidelines:

Start small: Begin with one module or subsystem rather than analyzing the entire architecture at once.

Document reasoning: Record identified change drivers and independence assessments to facilitate future decisions and team discussions.

Iterate and refine: As systems evolve, periodically reassess change drivers. What was once a single concern may split into multiple independent concerns as requirements mature.

Validate with stakeholders: Discuss identified change drivers with relevant stakeholders (product owners, operations teams, security officers) to ensure accurate understanding.

Use concrete scenarios: Test designs against specific change scenarios: "If we need to switch databases, what must change?" or "If we add a new payment method, what's affected?"

Respect existing boundaries: When working with legacy systems, identify where IVP violations create maintenance problems and prioritize addressing the most costly coupling.

The original paper acknowledges several limitations:

The most significant challenge is correctly identifying change drivers, which requires domain expertise, historical change analysis, and architectural experience. Misidentifying change drivers can lead to suboptimal architectural decisions.

While IVP provides a conceptual definition of causal cohesion, measuring it objectively remains difficult. Traditional structural metrics may not accurately reflect causal relationships.

As of 2025, IVP lacks extensive empirical validation through controlled studies. However, its logical derivation of established, empirically-validated principles provides indirect validation.

IVP optimization may conflict with other concerns:

• Performance optimization may require strategic coupling
• Security requirements may dictate specific structures
• Team boundaries may influence module organization
• Deployment constraints may affect granularity decisions

IVP does not prescribe the appropriate granularity for analysis (file, class, method, or module level), which depends on system architecture style, team organization, and other practical considerations.

• DOI: 10.5281/zenodo.17677316 - Official IVP paper on Zenodo

Category:Software architecture Category:Software design Category:Software engineering Category:Software development philosophies Category:Software design patterns