csc 205 software engineering i 1 overview - design introduction to design review of architectural...

CSC 205 Software Engineering I

1

Overview - Design

• Introduction to Design• Review of Architectural Design• Modules• Structured Design• Objects• Object-Oriented Design • Detailed Design• Integration Testing


2

Goals and Objectives

• Develop a coherent representation of a software system that will satisfy the requirements

• Identify inadequacies in the requirements• Develop review plan that demonstrates

coverage of the requirements– yields confidence in design

• Develop test plan that covers design – yields confidence in both design and

implementation


3

Introduction to Design

RequirementsChange

Validation

Revalidation

Validation

Verification

Testing

Requirements Analysis +Specification

DesignHow ?

Operation andMaintenance

Implementation andIntegration


4

Relationship to other lifecycle phases

• Requirements– Specifies the “what” not the “how”– Provides conceptual boundaries

¤ keeps design focused

• Implementation– Design stops and coding begins when design

specifications are sufficient for coding assignments

¤ each assignment, theoretically, can be given to a programmer unaware of the overall system architecture


5

Basic Design Process

• The design process develops several models of the software system at different levels of abstraction– Starting point is an informal “boxes and arrows” design– Add information to make it more consistent and

complete– Provide feedback to earlier designs for improvement

Informaldesignoutline

Informaldesign

More formaldesign

Finisheddesign


6

Top-Down Design

• Recursively partition a problem into sub-problems until tractable (solvable) problems are identified

System level

subsystem level

module level


7

Design Activities

• Architectural design– Subsystem identification

¤ services and constraints are specified

– Module design¤ modular decomposition is performed; relationships specified

• Detailed design– Interface design

¤ module interfaces are negotiated, designed and documented

– Data structure and algorithm design¤ module details (data structures and algorithms) to provide

system services are specified


8

Design Products

• Refined requirements specification• Description of systems to be constructed

– software architecture (diagram)– modular decomposition (hierarchy)– abstract module interface specifications– detailed module designs

• Documentation of decisions and rationale• Data dictionary of all defined objects• Validation review plan• Integration test plan


9

Desirable Characteristics/ Common Problems• Uniform• Complete• Rigorous • Confirmable,

verifiable, testable• Supportable by tools• Desensitized to

change• Accommodates

independent coding

• Depth-first design: only partial satisfaction of requirements

• Failure to consider potential changes

• Too detailed: overly constrains implementation

• Ambiguous: misinterpreted during implementation

• Undocumented: designers become essential

• Inconsistent: system cannot be integrated


10

Architectural Design

• Architectural Design – decomposition of large systems that provide some related

set of services + establishing a framework for control and communication

• Architectural styles establish guidelines – a relatively new area of research

• No generally accepted architectural design process – some important sub-processes

¤ System structuring: structuring of the system into a number of subsystems, where a subsystem is an independent software unit

¤ Control modeling: establishing a general model of control relationships between the parts of the system

¤ Modular decomposition: decomposing each identified subsystem into modules


11

Software Architecture

• Components– The elements out of which the system is built– Examples: filters, databases, objects, ADTs

• Connectors– The interaction or communication mechanisms– The glue that combines the components– Examples: procedure calls, pipes, event

broadcast, messages, secure protocols

• Constraints– Limitations on the composition of components and

connectors


12

Architectural Style

• Example architectural styles– Batch sequential– Pipe and filter– Main program and subroutines– Blackboard– Interpreter– Client-server– Communicating processes– Event systems– Object-oriented– Layered Systems

Families of systems defined by patterns of composition


13

Architectural Design:System Structuring• Model of the system structure and decomposition

¤ how subsystems share data¤ how they are distributed¤ how they interface with each other

• Three standard models– Repository model: how subsystems exchange and share

information• E.g., all shared data is held in a central database or each sub-system

maintains its own database

– Distribution model: how data and processing is distributed across a range of processors

• E.g., Client-server or peer-to-peer processes

– Abstract machine model: the interfacing of subsystems as abstract machines each of which provides a set of services to others

• E.g., each subsystem defines an abstract machine


14

Architectural Design:Control Modeling• Control of subsystems so that services are

delivered to the right place at the right time• Two general approaches

– Centralized control¤ One subsystem has overall responsibility for control and

starts/stops other subsystems• call-return model (sequential)• manager model (concurrent)

– Event-based control¤ each subsystem responds to externally generated events

(from other subsystems or the environment)• broadcast model• interrupt-driven model


15

Architectural Design:Modular decomposition• After decomposition of the system into subsystems,

subsystems must be decomposed into modules– No rigid distinction between system and modular decomposition

• Two important approaches for decomposing subsystems into modules:

– Data-flow (structured design)¤ system is decomposed into functional modules which accept input data

and transform it to output data ¤ process-based decomposition¤ achieves mostly procedural abstractions

– Object-oriented (object-oriented analysis and design)¤ system is decomposed into a set of communicating objects¤ object-based decomposition¤ achieves both procedural + data abstractions


16

Architectural Design:Hierarchy• Hierarchies support modular decomposition

– Uses relation: a uses b only if the correct functioning of a depends on the existence of a correct implementation of b

¤ modular decomposition can be specified by uses, where• Level 0 is the set of all programs that use no other program• Level i ( i > 0) is the set of all programs that use at least one

program on level i -1 and no program at level ≥ i.• Note: the uses relation does not always provide a hierarchy

– Is-composed-of relation: a is-composed-of b if b is a component of a and encapsulated within a

¤ modular decomposition can be specified by is-composed-of, where• non-terminals are virtual code• terminals are the only units represented by code• Then, the uses relation is specified over the set of terminals only• Note: the is-composed-of relation is acyclic


17

Modules

• Definition: a software entity encapsulating the representation of an abstraction and providing an abstract interface to it

• Module interaction– Module hides implementation details so that the rest of the

system is insulated and protected from the details AND vice versa– Modules communicate only through well-defined interfaces

• Negotiating module interfaces– design interface to component to be insensitive to change– determine likely usage patterns and purposes– disseminate minimal information as useful generalities

¤ abstract Interfaces: one specification, many possible implementations

– suppress unnecessary detail of a design decision


18

Architecture, Subsystems and Modules • Architecture consists of interacting subsystems

– determined by application domain

• Subsystems– a component whose operation does not depend on the

services provided by other subsystems– communicates with other subsystems via defined

interfaces– is decomposed further into modules by design methods

• Modules– a component that provides one or more services to

other modules– not normally considered an independent subsystem


19

Modular Decomposition:Abstraction• Abstraction is a tool that supports focus on important,

inherent properties and suppression of unnecessary detail– permits separation of conceptual aspects of a system from the

implementation details– allows postponement of design decisions

• Three basic abstraction mechanism– procedural abstraction

¤ specification describes input/output¤ implementation describes algorithm

– data abstraction¤ specification describes attributes, values, properties, operations¤ implementation describes representation and implementation

– control abstraction¤ specification describes desired effect¤ implementation describes mechanism


20

Modular Decomposition:Information Hiding• Information hiding is a decomposition principle that

requires that each module hides its internal details and is specified by as little information as possible– forces design units to communicate only through well-

defined interfaces– enables clients to be protected if internal details change

• Sample entities to encapsulate– abstract data types– algorithms– input and output formats– processing sequence– machine dependencies– policies (e.g. security issues, garbage collection, etc.)


21

Modular Decomposition:Cohesion and Coupling

• Cohesion– the degree to which the internals of a module are

related

• Coupling– the degree to which the modules of a design are

related

• The ideal system has highly cohesive modules that are loosely coupled– high cohesion -> well-designed reusable module– low coupling -> coherent design, resistant to

change


22

Types of Cohesion

• coincidental– multiple, completely unrelated actions

• logical– series of related actions, often selected by parameters

• temporal– series of actions related in time

• procedural– series of actions sharing sequence of steps

• communicational– procedural cohesion but on the same data

• informational– series of independent actions on the same data

• functional: exactly one action (Good)

(Bad)


23

Types of Coupling

• content– one module directly references content of another

• common– both modules have access to same global data

• control– one module passes an element of control to another

• stamp– one module passes a data structure to another;

which only uses part of the passed information

• data– one module passes only homogeneous data items

(Good)

(Bad)


24

Some examples of cohesion

• Logical cohesion– Input/Output libraries– Math libraries

• Temporal cohesion– Program initialization

• Communicational cohesion– “calculate data and write it to disk”– Closely related: sequential cohesion

¤ the output of one element is the input to another


25

Some examples of coupling

• Control coupling– One module passes control flags (parameters or

global variables) that control the sequence of processing steps in another module

• Stamp coupling (alternative definition)– Similar to common coupling (modules that share

global data) except that globals are shared selectively among routines that require the data

– Ada packages support stamp coupling since variables defined in a package specification are shared between all modules which use the package.


26

Structured Design

• System is completely specified by the functions that is to perform– Top-down, iterative refinement of functionality

¤ break the [system] function into subfunctions¤ determine hierarchy and data interaction

– Function refinement guides data refinement– Hierarchical organization is a tree with one module per

subfunction

• Pros and Cons– modules are highly functional– best suited when state information is not pervasive– data decisions must be made earlier– changes in data ripple through entire structure– little chance for reusability


27

Structured Design Process

• Identify flow of data and incorporate detail and structure iteratively– given specification loop

¤ identify data flow and transformations• nouns as data, verbs as transformations

¤ derive data flow diagrams¤ identify "natural aggregates"

• identify highest level input and output units• remaining units are central transforms• form level of structure chart• control module (coordinate)

– input module (afferent)– central module(s) (transform)– output module (efferent)

• form structure chart

– until implementation is immediate


28

Data Flow Diagrams

•Software system as flow of data from logical processing unit A (transformation) to B

– do not include control information– data flow diagram elements

¤ round-cornered rectangle = transformation¤ vector = data flow¤ vector operation = data flow link

• * (and) • + (or)• + (exclusive or)

¤ arc with data flow link = bracketing to override precedence

• and over or over exclusive or

¤ rectangle = data store¤ circle = user interaction (input/output)


29

Data Flow Templates

+

+

*A

A

A

B

B

B

C

C

Cconjunct

disjunct

exclusive

*

+

+A

A

A

A

B

C

B

B

C

C

C

simple

conjunct

disjunct

exclusive


30

Structure Charts

•Depict software structure as a hierarchy of modules and data communication

– may have control info defining selection and loops

– structure chart elements ¤ rectangle = module¤ four types of module based on data flow

• control (coordinate)• input (afferent)• central (transform)• output (efferent)


31

Structure Charts (cont’d)

¤ vector = control relationship¤ arrow with circular tail = directed data relationship

• data couple (open), control couple (closed)

¤ round-cornered rectangle = data store¤ circle = user interaction (input/output)


32

Structure Chart Templates

COORDINATETRANSFORM

AFFERENT EFFERENT

X

X

A B

Z

Z

D D


33

Overview - Object-Oriented Analysis and Design

• Introduction to OOAD• Introduction to Objects

– Background– Object-Oriented Programming– Classes and Objects– Object-Oriented Concepts

• Object Modeling Technique– Object-Oriented Analysis – Object-Oriented Design


34

Object-Oriented Approaches

• Object-Oriented Methodology– development approach used to build complex systems

using the concepts of object, class, polymorphism, and inheritance with a view towards reusability

– encourages software engineers to think of the problem in terms of the application domain early and apply a consistent approach throughout the entire life-cycle

• Object-Oriented Analysis and Design– analysis models the “real-world” requirements,

independent of the implementation environment– design applies object-oriented concepts to develop and

communicate the architecture and details of how to meet requirements


35

General Advantages

• Understandable– maps the “real-world” objects more directly– manages complexity via abstraction and encapsulation

• Practical– successful in real applications– suitable to many, but not all, domains

• Productive– experience shows increased productivity over life-

cycle– encourages reuse of model, design, and code

• Stable– changes minimally perturb objects


36

Advantages wrt “Principles”• Separation of concerns

– developers focus on common versus special properties of objects

• Modularity– specifically in terms of classification of objects

• Abstraction– allowing common versus special properties to be represented

separately

• Anticipation of change– new modules (objects) systematically specialize existing objects

• Generality– a general object may be specialized in several ways

• Incrementality– specific requirements (e.g. performance) may be addressed in

specializations


37

Object-oriented versus ClassicalSW development

Object-orientedAnalysis

Object-orientedDesign

Object-orientedImplementation

Maintenance

Requirements Analysis

Design

Implementation& Integration

Maintenance


38

Background - Objects

• Traditionally, programming has been“procedure-oriented”– Focus on process, algorithms, and tools– A system’s data has secondary importance

• Data and process considered separate– The data is external to a program; a program

reads it in, manipulates it, and then writes it out– Relationships between data types not considered

important¤ As a result, similarities were not leveraged leading to

duplication of code


39

Background, continued

• Problems– Lack of data encapsulation

¤ changes to a data format typically required major rewrites

– Poor models¤ Real world entities not well represented in requirements,

design and implementation• If an assumption about an entity changes, multiple modules

may have to change in response (since logic about an entity may be spread across modules)

– Low reuse¤ Procedure-oriented modules are often difficult to reuse

outside of their original development context


40

Object-Oriented Programming

• Objects combine both data and process– Increases the stature of data to be equivalent to

process

• Focus on real-world entities– Objects often represent real-world counterparts:

people, countries, calendars, cars, organizations, etc.

• Enables Categorization– Objects with high levels of abstraction can often be

specialized to more specific categories¤ For instance, car Honda Civic¤ or person athlete soccer player


41

Object-Oriented Programming, continued

• Addresses “Procedure-Oriented” Problems– Data and Process encapsulation

¤ Encapuslates data and related algorithms behind a single interface; both can be evolved without affecting other objects or modules (as long as the interface is constant)

– Natural Models¤ Objects can be used to appropriately structure a design

or accurately create a set of requirements based on knowlede about the real-world counterpart

– Increased Reuse¤ Well-designed object is often independent of the original

development context


42

Object name

state(attributes)

access to attributesMethod 1

Method 2

Method 3

Objects

• Data and operations are defined as a single unit

• Object :=– encapsulated state (attributes)– methods that exclusively control access to the

state


43

Classes

• Each object is an instance of a class• A class serves as a blueprint

– It defines the attributes and methods for the class– Thus, each object of a class has exactly the same

interface and set of attributes¤ each object can have different values for its attributes

Professor

Name: stringDept: string

get_name() return string....

class Professor

(Professor) (Professor)

Jean RaoulFrench

Debra RichardsonICS

get_name()....

get_name()....

Object instances of Professor


44

Object Model Notation: Introduction

Class Name

(Class Name)

InstanceVariable1InstanceVariable2: type

InstanceVariable1 = valueInstanceVariable2: type

Method1()Method2(arguments) return type

Method1()Method2(arguments) return type

Classes are represented as rectangles;

The class name is at the top, followed by attributes (instance variables) and methods (operations)

Depending on context some information can be hidden such as types or method arguments

Objects are represented as rounded rectangles;

The object’s name is its classname surrounded by parentheses

Instance variables can display the values that they have been assigned; pointer types will often point (not shown) to the object being referenced


45

Object Communication

• Objects communicate via method invocation– This is known as message passing– Legal messages are defined by the object’s interface– This interface is the only legal way to access another

object’s state

(Rectangle)

get_width

get_height

calculate_area

widthheight

Object


46

Objects: Terminology (partial review)

• Class– set of objects having the same methods and attributes– has a specification and an implementation– behavior is defined by the operations that can be

performed on objects belonging to the class

• Method– action that can be performed on any member of a class

• Encapsulation– packaging the specification and implementation of a class

so that the specification is visible and the implementation is hidden from clients

• Instantiation– the creation of a new object belonging to a class


47

Objects: Terminology, continued

• Aggregation– Objects representing components are associated with

an object representing their assembly (e.g. consists-of)– A mechanism for structuring object models

Weathermapping system

Weatherstation

Mapdatabase

Weathermap

consists of


48

Aggregation: example

consists of

consists of

Microcomputer

Monitor System box Mouse Keyboard

Chassis CPU RAM Fan


49


• Generalization– Allows a class, called a supertype, to be formed by factoring out

the common state and methods of several classes, called subtypes (is-a)

– Specialization is the converse case

Course

title

Undergrad Course

length

Postgrad Course

level

is-a

Supertype

Subtypes


50

Generalization: example

DogDog CatCat

AnimalAnimal

is-a

Enables the creation of lists whichcan consist of elements withdifferent types!

animalList: listOf(Animal)

animalList CatCat AnimalAnimal DogDog


51

Aggregation/ Generalization: example

consists of

consists of

Microcomputer

Monitor System box

Mouse Keyboard

Chassis CPU RAM Fan

Input Device

is-a

Color Monitor B/W Monitor

is-a

SpecializationGeneralization


52


• Inheritance– a subclass inherits methods and attributes from its superclass;

a subclass can also add additional operations and attributes;– e.g.: subclasses Undergrad Course and Postgrad Course

inherit the title attribute from the superclass Course

• Class hierarchy– generalization and inheritance are transitive across the

hierarchy– e.g. vehicle->automobile->4-door->Suburu Legacy; the legacy

inherits from 4-door, automobile, and vehicle

• Multiple Inheritance– subclass inherits operations and attributes from more than

one superclass (not a strict hierarchy)


53

Class Hierarchy - Single Inheritance: example

is-a

is-a

is-ais-a

Vehicle

LandVehicle WaterVehicle AirVehicle

truckcar

sailboat

airplane helicopter

motorboat ship yatch


54

Class Hierarchy - Multiple Inheritance: example

is-a

is-a

Vehicle

is-a

LandVehicle WaterVehicle

Car AmphibiousVehicle

Boat


55

Polymorphism

• A superclass defines an operation which its subclasses override.

• Via generalization a client may have a variable of the superclass type which is pointing to an instance of the subclass.

• When the operation is called, the subclass’s implementation is invoked


56

Polymorphism: example

is-a

is-a

Vehicle

is-a

LandVehicle WaterVehicle

Car AmphibiousVehicle

Boat

defines methodsturnOn() andturnOff()

Each subclass implements turnOn() and turnOff()


57

Polymorphism: example, continued

myVehicle: Vehicle := new Boat();

myVehicle->turnOn();

myVehicle->turnOff();

• In both cases, its Boat.turnOn() and Boat.turnOff() that’s executed!


58

Good and poor object classesDiscussion• Reasonableness of an object depends on the

problem at hand: the challenge of O-O analysis is to find relevant object classes

¤ Country: USA, Australia¤ State: California, Washington¤ Thermometer¤ Temperature: 32°F¤ Computer file¤ Swimming¤ Students¤ Class of students¤ Students whose middle initial is J¤ Inventory¤ Automobile Part¤ Part ID

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