software-design-philosophy

name	software-design-philosophy
description	Manage software complexity through deep modules, information hiding, and strategic programming. Use when the user mentions "module design", "API too complex", "shallow class", "complexity budget", or "strategic vs tactical". Covers deep vs shallow modules, red flags for complexity, and comments as design documentation. For code quality, see clean-code. For boundaries, see clean-architecture.
license	MIT
metadata	{"author":"wondelai","version":"1.0.0"}

A Philosophy of Software Design Framework

A practical framework for managing the fundamental challenge of software engineering: complexity. Apply these principles when designing modules, reviewing APIs, refactoring code, or advising on architecture decisions. The central thesis is that complexity is the root cause of most software problems, and managing it requires deliberate, strategic thinking at every level of design.

Core Principle

The greatest limitation in writing software is our ability to understand the systems we are creating. Complexity is the enemy. It makes systems hard to understand, hard to modify, and a source of bugs. Every design decision should be evaluated by asking: "Does this increase or decrease the overall complexity of the system?" The goal is not zero complexity -- that is impossible in useful software -- but to minimize unnecessary complexity and concentrate necessary complexity where it can be managed.

Scoring

Goal: 10/10. When reviewing or creating software designs, rate them 0-10 based on adherence to the principles below. A 10/10 means deep modules with clean abstractions, excellent information hiding, strategic thinking about complexity, and comments that capture design intent. Lower scores indicate shallow modules, information leakage, tactical shortcuts, or missing design documentation. Always provide the current score and specific improvements needed to reach 10/10.

The Software Design Framework

Six principles for managing complexity and producing systems that are easy to understand and modify:

1. Complexity and Its Causes

Core concept: Complexity is anything related to the structure of a software system that makes it hard to understand and modify. It manifests through three symptoms: change amplification, cognitive load, and unknown unknowns.

Why it works: By identifying the specific symptoms of complexity, developers can diagnose problems precisely rather than relying on vague notions of "messy code." The two fundamental causes -- dependencies and obscurity -- provide clear targets for design improvement.

Key insights:

• Change amplification: a simple change requires modifications in many places
• Cognitive load: a developer must hold too much information in mind to make a change
• Unknown unknowns: it is not obvious what needs to be changed, or what information is relevant (the worst symptom)
• Dependencies: code cannot be understood or modified in isolation
• Obscurity: important information is not obvious from the code or documentation
• Complexity is incremental -- it accumulates from hundreds of small decisions, not one big mistake
• The "death by a thousand cuts" nature of complexity means every decision matters

Code applications:

Context	Pattern	Example
Change amplification	Centralize shared knowledge	Extract color constants instead of hardcoding #ff0000 in 20 files
Cognitive load	Reduce what developers must know	Use a simple open(path) API instead of requiring buffer size, encoding, and lock mode
Unknown unknowns	Make dependencies explicit	Use type systems and interfaces to surface what a change affects
Dependency management	Minimize cross-module coupling	Pass data through well-defined interfaces, not shared global state
Obscurity reduction	Name things precisely	numBytesReceived not n; retryDelayMs not delay

See: references/complexity-symptoms.md

2. Deep vs Shallow Modules

Core concept: The best modules are deep: they provide powerful functionality behind a simple interface. Shallow modules have complex interfaces relative to the functionality they provide, adding complexity rather than reducing it.

Why it works: A module's interface represents the complexity it imposes on the rest of the system. Its implementation represents the functionality it provides. Deep modules give you a high ratio of functionality to interface complexity. The interface is the cost; the implementation is the benefit.

Key insights:

• A module's depth = functionality provided / interface complexity imposed
• Deep modules: simple interface, powerful implementation (Unix file I/O, garbage collectors)
• Shallow modules: complex interface, limited implementation (Java I/O wrapper classes)
• "Classitis": the disease of creating too many small, shallow classes
• Each interface adds cognitive load -- more classes does not mean better design
• The best abstractions hide significant complexity behind a few simple concepts
• Small methods are not inherently good; depth matters more than size

Code applications:

Context	Pattern	Example
Deep module	Hide complexity behind simple API	file.read(path) hides disk blocks, caching, buffering, encoding
Shallow module	Avoid thin wrappers that just pass through	A FileInputStream wrapped in BufferedInputStream wrapped in ObjectInputStream
Classitis cure	Merge related shallow classes	Combine RequestParser, RequestValidator, RequestProcessor into one RequestHandler
Method depth	Methods should do something substantial	A delete(key) that handles locking, logging, cache invalidation, and rebalancing
Interface simplicity	Fewer parameters, fewer methods	config.get(key) with sensible defaults, not 15 constructor parameters

See: references/deep-modules.md

3. Information Hiding and Leakage

Core concept: Each module should encapsulate knowledge that is not needed by other modules. Information leakage -- when a design decision is reflected in multiple modules -- is one of the most important red flags in software design.

Why it works: When information is hidden inside a module, changes to that knowledge require modifying only that module. When information leaks across module boundaries, changes propagate through the system. Information hiding reduces both dependencies and obscurity, the two fundamental causes of complexity.

Key insights:

• Information hiding: embed knowledge of a design decision in a single module
• Information leakage: the same knowledge appears in multiple modules (a red flag)
• Temporal decomposition causes leakage: splitting code by when things happen forces shared knowledge across phases
• Back-door leakage through data formats, protocols, or shared assumptions is the subtlest form
• Decorators are frequent sources of leakage -- they expose the decorated interface
• If two modules share knowledge, consider merging them or creating a new module that encapsulates the shared knowledge

Code applications:

Context	Pattern	Example
Information hiding	Encapsulate format details	One module owns the HTTP parsing logic; callers get structured objects
Temporal decomposition	Organize by knowledge, not time	Combine "read config" and "apply config" into a single config module
Format leakage	Centralize serialization	One module handles JSON encoding/decoding rather than spreading json.dumps everywhere
Protocol leakage	Abstract protocol details	A MessageBus.send(event) hides whether transport is HTTP, gRPC, or queue
Decorator leakage	Use deep wrappers sparingly	Prefer adding buffering inside the file class over wrapping it externally

See: references/information-hiding.md

4. General-Purpose vs Special-Purpose Modules

Core concept: Design modules that are "somewhat general-purpose": the interface should be general enough to support multiple uses without being tied to today's specific requirements, while the implementation handles current needs. Ask: "What is the simplest interface that will cover all my current needs?"

Why it works: General-purpose interfaces tend to be simpler because they eliminate special cases. They also future-proof the design since new use cases often fit the existing abstraction. However, over-generalization wastes effort and can itself introduce complexity through unnecessary abstractions.

Key insights:

• "Somewhat general-purpose" is the sweet spot between too specific and too generic
• The key question: "What is the simplest interface that will cover all my current needs?"
• General-purpose interfaces are often simpler than special-purpose ones (fewer special cases)
• Push complexity downward: modules at lower levels should handle hard cases so upper levels stay simple
• Configuration parameters often represent failure to determine the right behavior -- each parameter is complexity pushed to the caller
• When in doubt, implement the simpler, more general-purpose approach first

Code applications:

Context	Pattern	Example
API generality	Design for the concept, not one use case	A text.insert(position, string) API instead of text.addBulletPoint()
Push complexity down	Handle defaults in the module	A web server that picks reasonable buffer sizes instead of requiring callers to configure them
Reduce configuration	Determine behavior automatically	Auto-detect file encoding instead of requiring an encoding parameter
Avoid over-specialization	Remove use-case-specific methods	One store(key, value, options) instead of storeUser(), storeProduct(), storeOrder()
Somewhat general	General interface, specific implementation	A Datastore interface that currently backs onto PostgreSQL but does not expose SQL concepts

See: references/general-vs-special.md

5. Comments as Design Documentation

Core concept: Comments should describe things that are not obvious from the code. They capture design intent, abstraction rationale, and information that cannot be expressed in code. The claim that "good code is self-documenting" is a myth for anything beyond low-level implementation details.

Why it works: Code tells you what the program does, but not why it does it that way, what the design alternatives were, or what assumptions the code makes. Comments capture the designer's mental model -- the abstraction -- which is the most valuable and most perishable information in a system.

Key insights:

• Four types: interface comments, data structure member comments, implementation comments, cross-module comments
• Interface comments are the most important: they define the abstraction a module presents
• Write comments first (comment-driven design) to clarify your thinking before writing code
• "Self-documenting code" works only for low-level what; it fails for why, assumptions, and abstractions
• Comments should describe what is not obvious -- if the code makes it clear, don't repeat it
• Maintain comments near the code they describe; update them when the code changes
• If a comment is hard to write, the design may be too complex

Code applications:

Context	Pattern	Example
Interface comment	Describe the abstraction, not the implementation	"Returns the widget closest to the given position, or null if no widgets exist within the threshold distance"
Data structure comment	Explain invariants and constraints	"List is sorted by priority descending; ties are broken by insertion order"
Implementation comment	Explain why, not what	"// Use binary search here because the list is always sorted and can contain 100k+ items"
Cross-module comment	Link related design decisions	"// This timeout must match the retry interval in RetryPolicy.java"
Comment-driven design	Write the interface comment before the code	Draft the function's contract and behavior first, then implement

See: references/comments-as-design.md

6. Strategic vs Tactical Programming

Core concept: Tactical programming focuses on getting features working quickly, accumulating complexity with each shortcut. Strategic programming invests 10-20% extra effort in good design, treating every change as an opportunity to improve the system's structure.

Why it works: Tactical programming appears faster in the short term but steadily degrades the codebase, making every future change harder. Strategic programming produces a codebase that stays easy to modify over time. The small upfront investment compounds -- systems designed strategically are faster to work with after a few months.

Key insights:

• Tactical tornado: a developer who produces features fast but leaves wreckage behind; often celebrated short-term but destructive long-term
• Strategic mindset: your primary job is to produce a great design that also happens to work, not working code that happens to have a design
• The 10-20% investment: spend roughly 10-20% of development time on design improvement
• Startups need strategic programming most -- early design shortcuts compound into crippling technical debt as the team grows
• "Move fast and break things" culture (early Facebook) vs design-focused culture (Google) -- Google engineers were more productive on complex systems
• Every code change is an investment opportunity: leave the code a little better than you found it
• Refactoring is not a special event -- it is part of every feature's development

Code applications:

Context	Pattern	Example
Tactical trap	Resist quick-and-dirty fixes	Don't add a boolean parameter to handle "just this one special case"
Strategic investment	Improve structure during feature work	When adding a feature, refactor the module interface if it has become awkward
Tactical tornado	Recognize and intervene	A developer who writes 2x the code but creates 3x the maintenance burden
Startup discipline	Invest in design from day one	Clean module boundaries and good abstractions even under time pressure
Incremental improvement	Fix one design issue per PR	Each pull request improves at least one abstraction or eliminates one piece of complexity
Design reviews	Evaluate structure, not just correctness	Code reviews should ask "does this make the system simpler?" not just "does it work?"

See: references/strategic-programming.md

Common Mistakes

Mistake	Why It Fails	Fix
Creating too many small classes	Classitis adds interfaces without adding depth; each class boundary is cognitive overhead	Merge related shallow classes into deeper modules with simpler interfaces
Splitting modules by temporal order	"Read, then process, then write" forces shared knowledge across three modules	Organize around information: group code that shares knowledge into one module
Exposing implementation in interfaces	Callers depend on internal details; changes propagate everywhere	Design interfaces around abstractions, not implementations; hide format and protocol details
Treating comments as optional	Design intent, assumptions, and abstractions are lost; new developers guess wrong	Write interface comments first; maintain them as the code evolves
Configuration parameters for everything	Each parameter pushes a decision to the caller, increasing cognitive load	Determine behavior automatically; provide sensible defaults; minimize required configuration
Quick-and-dirty tactical fixes	Each shortcut adds a small amount of complexity; over time the system becomes unworkable	Invest 10-20% extra in good design; treat every change as a design opportunity
Pass-through methods	Methods that just delegate to another method add interface without adding depth	Merge the pass-through into the caller or the callee
Designing for specific use cases	Special-purpose interfaces accumulate special cases and become bloated	Ask "what is the simplest interface that covers all current needs?"

Quick Diagnostic

Question	If No	Action
Can you describe what each module does in one sentence?	Modules are doing too much or have unclear purpose	Split into modules with coherent, describable responsibilities
Are interfaces simpler than implementations?	Modules are shallow -- they leak complexity outward	Redesign to hide more; merge shallow classes into deeper ones
Can you change a module's implementation without affecting callers?	Information is leaking across module boundaries	Identify leaked knowledge and encapsulate it inside one module
Do interface comments describe the abstraction, not the code?	Design intent is lost; developers will misuse the module	Write comments that explain what the module promises, not how it works
Is design discussion part of code reviews?	Reviews only catch bugs, not complexity growth	Add "does this reduce or increase system complexity?" to review criteria
Does each module hide at least one important design decision?	Modules are organized around code, not around information	Reorganize so each module owns a specific piece of knowledge
Can a new team member understand module boundaries without reading implementations?	Abstractions are not documented or are too leaky	Improve interface comments and simplify interfaces until they are self-evident
Are you spending 10-20% of time on design improvement?	Technical debt is accumulating with every feature	Adopt a strategic mindset; include design improvement in every PR

Reference Files

• complexity-symptoms.md: Three symptoms of complexity, two causes, measuring complexity, the incremental nature of complexity
• deep-modules.md: Deep vs shallow modules, interface-to-functionality ratio, classitis, designing for depth
• information-hiding.md: Information hiding principle, information leakage red flags, temporal decomposition, decorator pitfalls
• general-vs-special.md: Somewhat general-purpose approach, pushing complexity down, configuration parameter antipattern
• comments-as-design.md: Four comment types, comment-driven design, self-documenting code myth, maintaining comments
• strategic-programming.md: Strategic vs tactical mindset, tactical tornado, investment approach, startup considerations

Further Reading

This skill is based on John Ousterhout's practical guide to software design. For the complete methodology with detailed examples:

• "A Philosophy of Software Design" by John Ousterhout (2nd edition)

About the Author

John Ousterhout is the Bosack Lerner Professor of Computer Science at Stanford University. He is the creator of the Tcl scripting language and the Tk toolkit, and co-founded several companies including Electric Cloud and Clustrix. Ousterhout has received numerous awards, including the ACM Software System Award, the UC Berkeley Distinguished Teaching Award, and the USENIX Lifetime Achievement Award. He developed A Philosophy of Software Design from his CS 190 course at Stanford, where students work on multi-phase software design projects and learn to recognize and reduce complexity. The book distills decades of experience in building systems software and teaching software design into a concise set of principles that apply across languages, paradigms, and system scales. Now in its second edition, the book has become a widely recommended resource for software engineers seeking to improve their design skills beyond correctness and into clarity.