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The Complexity of Programming Models. Grady Booch IBM Fellow. How much software exists in the world?. SLOC is a measure of labor (not of value) Old code never dies (you have to kill it) Some code is DOA Some assumptions 1 SLOC = 1 semicolon Number of software professionals worldwide

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how much software exists in the world
How much software exists in the world?
  • SLOC is a measure of labor (not of value)
    • Old code never dies (you have to kill it)
    • Some code is DOA
  • Some assumptions
    • 1 SLOC = 1 semicolon
    • Number of software professionals worldwide
    • %of software professionals who cut code
    • SLOC/developer/year
    • $100US/SLOC
dimensions of software complexity

Defense Weapon System



National Air Traffic

Control System










Small Scientific


IS Application

Distributed Objects

(Order Entry)


MIS System

Enterprise IS

(Family of IS


IS Application


(Order Entry)



Dimensions of software complexity

Higher technical complexity

- Embedded, real-time, distributed, fault-tolerant

- Custom, unprecedented, architecture reengineering

- High performance

An average software project

- 5-10 people

- 3-9 month duration

- 3-5 external interfaces

- Some unknowns & risks




- Small scale

- Informal

- Single stakeholder

- “Products”




- Large scale

- Contractual

- Many stake holders

- “Projects”

Lower technical complexity

- Mostly 4GL, or component-based

- Application reengineering

- Interactive performance


stakeholders and views
Stakeholders and views
  • A given system is many things to many different stakeholders
    • End user
    • Customer
    • Sys admin
    • Project manager
    • System engineer
    • Developer
    • Architect
    • Maintainer
    • Tester
    • Other systems
  • Multiple realities, multiple views and multiple blueprints exist
simplicity has different points of view
Simplicity has different points of view
  • User
    • Simple metaphors and gestures
  • End-user programmer
    • Access to significant parts and flexible mechanisms for behavior
  • Architect
    • Common architectural patterns
  • Developer
    • Common design patterns and idioms
  • Tester
    • Access to significant parts
simplicity has different points of view1
Simplicity has different points of view
  • Business analyst
    • Clear separation of rules
  • Database analyst
    • Purity of semantics
  • Security officer
    • Clear and perfectly executed policies
  • Administrator
    • Clear separation of components

In the presence of essential complexity,

establishing simplicity in one part of a system

requires trading off complexity in another

simplicity in languages
Simplicity in languages
  • Tradeoff between primitiveness and convenience
    • Control structures
  • Tradeoff between explicitness and abstraction
    • Java garbage collection
  • Tradeoff between performance of development and performance of execution
    • VisualBasic
    • Smalltalk
  • Tradeoff between packaging for design versus packaging for development versus packaging for deployment
    • Beans
    • Services
    • Aspects

A programming model specifies the

semantic universe within which the developer labors

and is defined by the languages, platforms, tools,

and best practices of that constellation

web centric programming model
Web-centric programming model
  • Platforms
    • Linux
    • Apache
    • MySQL
    • J2EE
  • Best practices
    • Coding
    • Design patterns
    • Deployment
    • User interface
    • Accessibility
    • Internationalization
    • Security
    • Logging
    • Backup
  • Tools
    • Eclipse
    • Dreamweaver
    • Photoshop
    • ClearCase
    • ClearQuest
    • RSA
    • Portfolio Manager
    • RequisitePro
    • Tester
  • Languages
    • HTML
    • CSS
    • XSL
    • XML
    • SQL
    • RSS
    • Java
    • JavaScript
    • PHP
    • Flash
    • UML
alternative programming models
Alternative programming models
  • Game developer
  • High performance computing
  • Command and control
  • Artificial intelligence
  • Domain-specific frameworks

Handbook of Software Architecture,


A system is shaped by a myriad of

design decisions by different stakeholders

that work to balance the forces

swirling around the system

forces in civil architecture





Forces in civil architecture

Kinds of loads

- Dead loads

- Live loads

- Dynamic loads

Avoiding failure

- Safety factors

- Redundancy

- Equilibrium

Any time you depart from established practice, make ten times the

effort, ten times the investigation. Especially on a very large project.

- LeMessuier

why is software inherently complex
Why is software inherently complex?
  • Complexity of the problem domain
  • Difficulty of managing the development process
  • Fluidity of software
  • Fundamental challenges of discrete systems
complexity of the problem domain
Complexity of the problem domain
  • Volume of requirements
  • Presence of competing/contradictory requirements
  • Non-functional requirements that push the limits of software
  • Requirements churn
  • Difficulty of communicating requirements
  • Impedance mismatch among stakeholders
  • Unrestrained external complexity
  • Software drag
difficulty of managing the development process
Difficulty of managing the development process
  • Software as a team sport
  • Presence of multiple languages, platforms, processes, architectures, and tools
  • Issues of scalability
  • Technology churn
  • Size up
    • Increasing database size by a factor of x increases query response time by at most a factor of x.
  • Speed up
    • Increasing the capacity of your hardware configuration by a factor of x decreases your query response time by no less than a factor of x.
  • Scale up
    • Increasing the workload on your system by a factor of x while maintaining response time and/or throughput requires increasing your capacity by a factor of no more than x.
  • Scale out
    • Increasing workers by a factor of x requires replicating your capacity by a factor of at most x.

fluidity of software
Fluidity of software
  • Software springs from pure thought and is intrinsically malleable, yet it can be made manifest in our hardware systems, limited only by our vision (and certain immutable laws of physics and software)
fundamental challenges of discrete systems
Fundamental challenges of discrete systems
  • Non-continuous behavior of discrete systems
  • Combinatorial explosion of states
  • Corruption from unexpected external events
  • Lack of mathematical tools and intellectual capacity to model the behavior of large discrete systems
essential complexity
Essential complexity
  • “Einstein argued that there must be simplified explanations of nature, because God is not capricious or arbitrary. No such faith comforts the software engineer. Much of the complexity that he must master is arbitrary complexity.” [Brooks]
  • We may master essential complexity, but we can never make it go away.
measuring complexity of biological systems syntactic
Measuring complexity of biological systems (syntactic)
  • Kolmogorov
  • Entropy
  • Mean component size
  • Number of behaviors exhibited
  • Species richness, relative to tolerance to risk
  • Species guilds
  • Energy flow
  • Grammatical complexity
  • Number of feedback loops
  • Cyclomatic measures (arcs, vertices, and components)
  • Graph complexity
  • Hamming distance

measuring complexity of biological systems semantic
Measuring complexity of biological systems (semantic)
  • Wordcount of description
  • Minimal description length (Rissanen)
  • Measure of environmental knowledge
  • Evolvability
  • Eigenbasis/measure of survivable environmental states
  • Program complexity

measuring complexity of software intensive systems
Measuring complexity of software-intensive systems
  • Kolmogorov
    • Relative size of a program capable of generating a given string
  • Entropy
    • Enumeration of states and transitions

measuring simplicity
Measuring simplicity
  • If we don’t know how to measure complexity, it is reasonable to suggest that we don’t know how to measure simplicity
  • “I can’t define it, but I know it when I see it.” [Supreme Court Justice Brennan]
  • Elegance is not an approach to finding a solution to a problem, it is the label we stick on the optimum solution
  • Elegance is doing the most with the least
  • Elegance means simplicity and less new code.
  • An elegant solution solves the whole problem." [Fisher, p. 37]

Fisher & Gipson, “In Search of Elegance”

triggers of complexity
Triggers of complexity
  • Significant interactions
  • High number of parts and degrees of freedom
  • Nonlinearity
  • Broken symmetry
  • Nonholonomic constraints
    • Localized transient anarchy

Flood, et al, Dealing with Complexity

attributes of a complex system
Attributes of a complex system
  • “Frequently, complexity takes the form of a hierarchy, whereby a complex system is composed of interrelated subsystems that have in turn their own subsystems, and so on, until some lowest level of elementary components is reached.” [Courtois]
  • Hierarchic systems are decomposable if they can be divided into identifiable parts; they are nearly decomposable if their parts are not completely independent. [Simon]
attributes of a complex system1
Attributes of a complex system
  • The choice of what components in a system are primitive is relative arbitrary and is largely up to the discretion of the observer of the system.
  • As systems evolve, objects that we once considered complex become the primitive objects upon which more complex systems are built.
attributes of a complex system2
Attributes of a complex system
  • Intracomponent linkages are generally stronger than intercomponent linkages. This fact has the effect of separating the high-frequency dynamics of the components – involving the internal structure of the components – from the low-frequency dynamics - involving interaction among components. [Simon]
attributes of a complex system3
Attributes of a complex system
  • Hierarchic systems are usually composed of only a few different kinds of subsystems in various combinations and arrangements. [Simon]
decomposible and nearly decomposible systems
Decomposible and nearly-decomposible systems
  • Vertically, the components of a complex system tend to be organized in increasing layers of abstraction
  • Horizontally, the components of a complex system tend to be clustered according to frequency
  • Both vertically and horizontally, the most resilient systems tend to exhibit loose coupling and tight cohesion among components

Simon, The Organization of Complex Systems

  • Loosely-coupled components adapt more easily to change
  • Loosely-coupled components permit time- and space-separation of processing
  • Overall flexibility can be enhanced by limiting the number of different kinds of components in the system (the system’s alphabet)
  • Alphabets are necessary but insufficient
  • Complex systems also require common languages, defining semantics of structural organization of alphabetic elements and interactional behavior among structures

Simon, The Organization of Complex Systems

  • Must have sufficient variety in its primitive processes so that no meaning is absolutely excluded from expression
  • Must have sufficient flexibility in its rules of combination so that any nuance can be expressed by building up composite structures

Simon, The Organization of Complex Systems

attributes of complex systems
Attributes of complex systems
  • Complex systems will evolve from simple systems much more rapidly if there are stable intermediate forms than if there are not. [Simon]
  • A complex system that works is invariable found to have evolved from a simple system that worked. A complex system designed from scratch never works and cannot be patched up to make it work. You have to start over, beginning with a working simple system. [Gall]
complex adaptive systems
Complex adaptive systems
  • Emergent behavior
  • Attributes
    • Classification of components
    • Identity of objects
    • Non-linearity of behavior
    • Flow of objects
    • Diversity
    • Use of internal models
    • Clustering

Holland, Hidden Order

characteristics of self organizing systems
Characteristics of self-organizing systems
  • Dynamic, requiring continual interactions among lower-level components to produce and maintain structure
  • Exhibit bifurcation leading to multistable systems
    • Strange attractors

Sante Fe Institute

self organization in biological systems
Self-organization in biological systems
  • Pattern formation in slime molds
  • Feeding aggregation of bark beetles
  • Synchronized flashing among fireflies
  • Fish schooling
  • Nectar source selection by honey bees
  • Trail formation in ants
  • Swarm raids of army ants
  • Colony thermoregulation in honey bees
  • Comb patterns in honey bee colonies
  • Wall building by ants
  • Termite mount building
  • Construction Aagorithms by wasps
  • Dominance hierarchies in paper wasps

Camazine, et al, Self-Organization in Biological Systems

creating order in biological systems
Creating order in biological systems
  • Self-organization
    • Emergence of patterns at the global level solely from numerous interactions among lower-level components of the system, the rules for which are executed using only local information
  • Imposed organization
    • Direction from a supervisory leader
    • Organic blueprints or recipes
    • Patterns in the environment

Camazine, et al, Self-Organization in Biological Systems

complexity functionality and understandability
Complexity, Functionality, and Understandability




shearing layers of change

Space plan






Shearing layers of change

Brand, How Buildings Learn

fundamentals of well engineered software intensive systems
Fundamentals of well-engineered software-intensive systems
  • Crisp abstractions
  • Clear separation of concerns
  • Balanced distribution of responsibilities
  • Simplicity via common abstractions and mechanisms
  • All abstractions are context-dependent
  • All non-trivial abstractions are, to some degree, leaky (and leaky abstractions are problematic). [Joel on Software]
  • There is no such thing as a perfect abstraction
    • Perfect is the enemy of good enough
worse is better
Worse Is Better
  • Simplicity is the most important consideration in a design.Both implementation and interface must be simple, though it is more important for the implementation to be simple.
  • The design must be correct in all observable aspects; it is slightly better to be simple than correct.
  • The design must not be overly inconsistent; it is better to drop those parts of the design that deal with less common circumstances than to introduce implementational complexity.
  • The design must cover as many imporatant situations as practical; completeness can be sacrificed in favor of any other quality.

Gabrial, “Worse is Better”

loose abstractions
Loose abstractions
  • Over-engineering a solution is the most common approach to dealing with complexity, yet it typically leads to total implosion.
  • Software which is flexible, simple, sloppy, tolerant and altogether forgiving turns out to be most resilient. [Bosworth]

The entire history of software engineering

Is one of rising levels of abstraction







Assembly -> Fortran/COBOL -> Simula -> C++ -> Java

Naked HW -> BIOS -> OS -> Middleware -> Domain-specific

Waterfall -> Spiral -> Iterative -> Agile

Procedural -> Object Oriented -> Service Oriented

Early tools -> CLE -> IDE -> XDE -> CDE

Individual -> Workgroup -> Organization

attacking complexity
Attacking complexity
  • Fundamentals
    • Crisp abstractions
    • Clear separation of concerns
    • Balanced distribution of responsibilities
    • Simplicity via common abstractions and mechanisms
  • Relax a constraint
  • Make assumptions
architecture defined
Architecture defined
  • Architecture n (1563)
    • The art or science of building or constructing edifices of any kind for human use
    • The action or process of building
    • Architectural work; structure, building
    • The special method of ‘style’ in accordance with which the details of the structure and ornamentation of a building are arranged
    • Construction or structure generally
    • The conceptual structure and overall logical organization of a computer or computer-based system from the point of view of its use or design; a particular realization of this

Oxford English Dictionary, 2nd ed.

physical systems
Physical systems
  • Mature physical systems have stable architectures
    • Aircraft, cars, and ships
    • Bridges and buildings
  • Such architectures have grown over long periods of time
    • Trial-and-error
    • Reuse and refinement of proven solutions
    • Quantitative evaluation with analytical methods
  • Mature domains are dominated by engineering efforts
    • Analytical engineering methods
    • New materials
    • New manufacturing processes
software intensive system
Software-intensive system
  • A system in which software is the dominant, essential, and indispensable element
    • E-commerce system
    • IT (business) system
    • Telephone switch
    • Flight control system
    • Real-time control system (e.g. industrial robot)
    • Sophisticated weapons system
    • Software development tools
    • System software (e.g. operating systems or compilers)
architecting software is different
Architecting software is different
  • No equivalent laws of physics
  • Transparency
  • Complexity
    • Combinatorial explosion of state space
    • Non-continuous behavior
    • Systemic issues
  • Requirement and technology churn
  • Low replication and distribution costs
architecture defined1
Architecture defined
  • Software architecture is what software architects do


architecture defined2
Architecture defined
  • Perry and Wolf, 1992
    • A set of architectural (or design) elements that have a particular form
  • Boehm et al., 1995
    • A software system architecture comprises
      • A collection of software and system components, connections, and constraints
      • A collection of system stakeholders' need statements
      • A rationale which demonstrates that the components, connections, and constraints define a system that, if implemented, would satisfy the collection of system stakeholders' need statements
  • Clements et al., 1997
    • The software architecture of a program or computing system is the structure or structures of the system, which comprise software components, the externally visible properties of those components, and the relationships among them

common elements
Common elements
  • Architecture defines major components
  • Architecture defines component relationships (structures) and interactions
  • Architecture omits content information about components that does not pertain to their interactions
  • Behavior of components is a part of architecture insofar as it can be discerned from the point of view of another component
  • Every system has an architecture (even a system composed of one component)
  • Architecture defines the rationale behind the components and the structure
  • Architecture definitions do not define what a component is
  • Architecture is not a single structure -- no single structure is the architecture
architecture defined3
Architecture defined
  • Architecture establishes the context for design and implementation





Architectural decisions are the most fundamental decisions; changing them will have significant ripple effects.

architecture defined4
Architecture defined
  • IEEE 1471-2000
    • Software architecture is the fundamental organization of a system, embodied in its components, their relationships to each other and the environment, and the principles governing its design and evolution
  • Software architecture encompasses the set of significant decisions about the organization of a software system
    • Selection of the structural elements and their interfaces by which a system is composed
    • Behavior as specified in collaborations among those elements
    • Composition of these structural and behavioral elements into larger subsystems
    • Architectural style that guides this organization

Booch, Kruchten, Reitman, Bittner, and Shaw

architecture defined5
Architecture defined
  • Software architecture also involves
    • Functionality
    • Usability
    • Resilience
    • Performance
    • Reuse
    • Comprehensibility
    • Economic and technology constraints and tradeoffs
    • Aesthetic concerns
architectural style defined
Architectural style defined
  • Style is the classification of a system’s architecture according to those with similar patterns
  • A pattern is a common solution to a common problem; patterns may be classified as idioms, mechanisms, or frameworks
model views concerns and stakeholders
Model, views, concerns, and stakeholders
  • A model is a simplification of reality, created in order to better understand the system being created; a semantically closed abstraction of a system
  • A view is a representation of a whole system from the perspective of a related set of concerns
  • A concern is those interests which pertain to the system's development, its operation or any other aspects that are critical or otherwise important to one or more stakeholders
  • A stakeholder is an individual, team, or organization (or classes thereof) with interests in, or concerns relative to, a system
stakeholders and views1
Stakeholders and views
  • Architecture is many things to many different stakeholders
    • End user
    • Customer
    • Sys admin
    • Project manager
    • System engineer
    • Developer
    • Architect
    • Maintainer
    • Tester
    • Other systems
  • Multiple realities, multiple views and multiple blueprints exist
representing software architecture

System integrators




Representing software architecture


Implementation View


Configuration management



Use Case View

Process View

Deployment View

System engineering

System topology





Clements, et al, Documenting Software Architectures

adapting views
Adapting views
  • Not all systems require all views
    • Single process (ignore process view)
    • Small program (ignore implementation view)
    • Single processor (ignore deployment view)
  • Some systems require additional views
    • Data view
    • Security view
    • Other aspects
logical view
Logical view
  • The view of a system’s architecture that encompasses the vocabulary of the problem and solution space, the collaborations that realize the system’s use cases, the subsystems that provide the central layering and decomposition of the system, and the interfaces that are exposed by those subsystems and the system as a whole
  • Focuses on
    • Functionality
    • Key Abstractions
    • Mechanisms
    • Separation of concerns and distribution of responsibilities
process view
Process view
  • The view of a system’s architecture that encompasses the threads and processes that form the system’s concurrency and synchronization mechanisms
  • Focuses on
    • Performance
    • Scalability
    • Throughput
implementation view
Implementation view
  • The view of a system's architecture that encompasses the components used to assemble and release the physical system
  • Focuses on
    • Configuration management
deployment view
Deployment view
  • The view of a system’s architecture that encompasses the nodes that form the system’s hardware topology on which the system executes
  • Focuses on
    • Distribution
    • Communication
    • Provisioning
use case view
Use case view
  • The view of a system’s architecture that encompasses the use cases that describe the behavior of the system as seen by its end users and other external stakeholders
relations among views
Relations among views

Logical view

Implementation view

Process view

Deployment view

the architecture of biological systems
The architecture of biological systems
  • Gene
  • Cell component
  • Cell
  • Tissue
  • Organ
  • System
cross cutting concerns in biological systems
Cross-cutting concerns in biological systems
  • Gene
    • Reproduction
    • Protein creation
  • Cell component (mitochondria)
    • Metabolism
    • Glutamate-mediated excitotic neurlogical injury
    • Cellular proliferation
    • Regulation of the cellular redox state
    • Heme synthesis
    • Heat production
cross cutting concerns in biological systems1
Cross-cutting concerns in biological systems
  • Cell
    • Structure
    • Metabolism
    • Reproduction
    • Protein synthesis
    • Transport/container
  • Tissue
    • Structure
    • Work
    • Transport/container
cross cutting concerns in biological systems2
Cross-cutting concerns in biological systems
  • Organ (liver)
    • Digestion
    • Carbohydrate metabolism
    • Glucoenogenesis
    • Glycogenesis
    • Breakdown of insulin
    • Lipid metabolism
    • Cholesterol synthesis
    • Production of triglycerides
    • Coagulation factors
    • Neutralization of various products
    • Storage of glucose and various vitamins
    • Red cell production for the fetus
  • System (circulatory)
    • Transport
    • Heat regulation
    • Healing mechanism
the reification of concerns
The reification of concerns
  • Concerns are not isomorphic to structure
  • In biological systems, these aspects evolved simultaneously and interdependently at each level of abstraction
    • They existed a priori as reactions to evolutionary forces
    • Post hoc we can name them
representing software architecture1

System integrators




Representing software architecture


Implementation View


Configuration management



Use Case View

Process View

Deployment View

System engineering

System topology





Clements, et al, Documenting Software Architectures

cross cutting concerns in software intensive systems
Cross-cutting concerns in software-intensive systems
  • Some structures and behaviors crosscut components
      • Security
      • Concurrency
      • Caching
      • Persistence
  • Such elements usually appear as small code fragments sprinkled throughout a system
  • Such elements are hard to localize using traditional approaches
the role of aspect oriented software development
The role of aspect-oriented software development
  • Remember the fundamentals
    • Crisp abstractions
    • Clear separation of concerns
    • Balanced distribution of responsibilities
    • Simplicity via common abstractions and mechanisms
  • The current sweet spot for aspects involves elements of each of these fundamentals
    • Especially with regard to building crisp abstractions and the separation of concerns for roles relative to packaging
    • This impacts primarily the interplay of the logical view and the use case view
what s missing what s next
What’s missing/what’s next
  • Remember the already complex programming model
    • Don’t make it more complex by adding yet another orthogonal mechanism
  • The current pragmatic focus is upon transformation tools that focus on already visible artifacts
    • The harder focus - plus the one that is most disruptive yet most potentially valuable - is upon transformation tools that focus on deep semantic representations and then the creation of these traditional artifacts by reflection
      • I.e. source code as a pretty-printed side-effect, not a central object
  • This stuff is fundamentally, wickedly hard
    • And it’s not going to get any better in my lifetime
      • And I plan on having a long life
  • Remember that the world doesn’t need Yet More Technology
    • We need less
      • And ultimately, the best technology is invisible
bibliography on complexity
Bibliography on complexity

Allen, T. & Starr, T., Hierarchy: Perspectives for Ecological Complexity, University of Chicago: 1982.

Axelrod, R., The Complexity of Cooperation, Princeton: 1997.

Barrow, J., Davies, P., & Harper, C., Science and Ultimate Reality, Cambridge University Press: 2004.

Bowker, G. & Star, S., Sorting Things Out: Classification and its Consequences, MIT Press: 1999.

Buchanan, M., Nexus, Norton: 2002.

Camazine, S., Deneubourg, J., Franks, N., Sneyd, J., Theraulaz, G., & Bonabeau, E., Self-Organization in Biological Systems, Princeton: 2001.

Duda, R., Pattern Classification, 2nd ed., Wiley: 2001.

Epxtein, J. & Axtell, R., Growing Artificial Societies, MIT Press: 1996.

Flood, R. & Carson, E., Dealing With Complexity: An Introduction to the Theory and Application of Systems Science, Plenum Press: 1988.

Gleick, J., Chaos: Making a New Science, Penguin Books: 1987.

Hollland, J., Hidden Order, Perseus Books: 1995.

Johnson, S., Emergence, Scribner: 2001.

biography on complexity
Biography on complexity

Kauffman, S., At Home in the Universe, Oxford University Press: 1995.

Kipfer, B., The Order of Things, MJF Books: 2001.

Lakoff, G., Women, Fire, and Dangerous Things: What Categories Reveal about the Mind, University of Chicago: 1987.

Lakoff, G. & Johnson, M., Metaphors We Live By, University of Chicago: 1980.

Markman, E., Categorization and Naming in Children, MIT Press: 2002.

Nicolis, G. & Prigogine, I., Exploring Complexity, Freeman: 1989.

Pattee, H., Hierarchy Theory: The Challenge of Complex Systems, George Braziller: 1973.

Prigogine, I., The End of Certainty, Free Press: 1996.

Simon, H., The Sciences of the Artificial, 2nd ed., MIT Press: 1969.

Waldrop, M., Complexity: The Emerging Science at the Edge of Order and Chaos, Simon & Schuster: 1992.