software system modelling

7/31/2019 Software System Modelling

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Software system modelling



Requirements Analysis

• Requirement analysis• Specifies sw’s operational characteristics

• Indicates sw’s interface with the other system elements

• Establishes constraints that sw must meet

• Requirements analysis allow the sw engineer (calledanalyst or modeler in his role) to

• Elaborate on basic requirements established during earlier

requirement engineering tasks

• Build models that depict user scenarios, functional activities,

problem classes and their relationships, system and class behavior,

and the flow of data as it is transformed.



System Modelling• Used in two development phases

• Analysis (what)

– High level of abstraction

• Design (what and how)

– In more detail terms

– low level of abstraction



System Modeling Methods

• Process Oriented methods (process-driven

systems):

• Flowcharts

• Data Flow Diagrams (DFD)

• Data oriented methods( data- driven system)

• Entity relationship diagram(ERD)

• Data Dictionary (DD)



System models weaknesses

• They do not model non-functional system

requirements

• They do not usually include information aboutwhether a method is appropriate for a given

problem

• They may produce too much documentation

• The system models are sometimes too detailed

and difficult for users to understand



Model types• Data processing model showing how the data is processed at

different stages

• Composition model showing how entities are composed of other

entities

• Architectural model showing principal sub-systems

• Classification model showing how entities have common

characteristics

• Stimulus/response model showing the system’s reaction to

events



Context models

• Context models are used to illustrate the

boundaries of a system

• Social and organisational concerns may

affect the decision on where to position

system boundaries

• Architectural models show the a systemand its relationship with other systems



The context of an ATM system

Auto-teller

system

Security

system

Maintenance

system

Account

da tabase

Usage

database

Branch

accounting

system

Branch

counter

system



ATM stakeholders

• Bank customers

• Representatives of other banks

• Bank managers

• Counter staff

• Database administrators

• Security managers

• Marketing department

• Hardware and software maintenance engineers

• Banking regulators



Process models

• Process models show the overall process

and the processes that are supported by the

system

• Data flow models may be used to show the

processes and the flow of information from

one process to another



Equipment procurement process

Get costestimates

Acceptdelivery of equipment

Check delivered

items

Validatespecification

Specifyequipmentrequired

Choosesupplier

Placeequipment

order

Installequipment

Findsuppliers

Supplier database

Acceptdelivered

equipment

Equipmentdatabase

Equipment

spec.Checked

spec.

Deliverynote

Deliverynote

Order

notification

Installation

instructions

Installationacceptance

Equipmentdetails

Checked andsigned order form

Order details +

Blank order form

Spec. +supplier +estimate

Supplier listEquipmentspec.



Behavioural models

• Behavioural models are used to describe the

overall behaviour of a system

• Two types of behavioural model – Data processing models that show how data is

processed as it moves through the system

– State machine models that show the systems

response to events

• Both of these models are required for a

description of the system’s behaviour



Data-processing models

• Data flow diagrams are used to model the

system’s data processing

• These show the processing steps as data flowsthrough a system

• IMPORTANT part of many analysis methods

• Simple and intuitive notation that customers

can understand

• Show end-to-end processing of data



DFD

• A traditional way of system requirement structuring• DFD is a picture of the movement of data between external

entities and the processes and data stores within a system

• In particular, analysing and designing e IPOSC

requirements of a system• Input

• Processing

• Output

• Storage

• Control



Data flow diagrams

• DFDs model the system from a functional perspective

• Tracking and documenting how the data

associated with a process is helpful todevelop an overall understanding of the

system

• Data flow diagrams may also be used inshowing the data exchange between a

system and other systems in its environment



Order processing DFD

Completeorder form

Order details + blank

order form

Validateorder

Recordorder

Send tosupplier

Adjustavailable budget

Budgetfile

Ordersfile

Completedorder form

Signedorder form

Signedorder form

Checked andsigned order

+ order notification

Order amount

+ accountdetails

Signedorder form

Order details



Slide 17

DFD Shapes from Visio

From Flow Chart /Data Flow Diagram

Process

Data Store

External Entity

From Software Diagram /Gane-Sarson DFD

Process

ID #

ID#

ExternalEntity

Data Store1

External

Entity

Data Store

Process

From Flow Chart /

Data Flow Diagram

Visio 5.x Visio 2000



Insulin pump DFD



Context Diagram

• Highest level view of the system

• Contains ONLY one process which

represents the ENTIRE system

• Also contains external data soures/sinks

• Plus data flows between external entities

and processes

• It contains NO data storage



DFD Guidelines

• Always begin with a context level diagram (also called level 0)

• Always show external entities at level 0

• Always label data flow arrows

• Do not represent procedural logic

• DFD evolves through a number of levels in

detail



Rules for Using DFD Symbols

• Data Flow That Connects

YES NOA process to another process

A process to an external entity

A process to a data store

An external entity to another external entity

An external entity to a data store

A data store to another data store



Slide 22

Relationship Among DFD levels



Decomposition Diagram



Example of DFD

Precision Tools sells a line of high-qualitywoodworking tools. When customers place orders on

the company’s Web site, the system checks to see if

the items are in stock, issues a status message to the

customer, and generates a shipping order to the

warehouse, which fills the order. When the order is

shipped, the customer is billed. The system also

produces various reports.• Draw a context diagram for the order system

• Draw DFD diagram 0 and 1 for the order system



ACCOUNTING

WAREHOUSECUSTOMER

0

Order

System

Order

Payment

In-Stock

Request

Status

Message

Invoice Shipping Confirmation

Shipping

Order

InventoryReports

Context Diagram of Order System



Lower-Level Diagrams• Functional Decomposition

– An iterative process of breaking a system descriptiondown into finer and finer detail

– Uses a series of increasingly detailed DFDs to describe

an IS

• Balancing

– The conservation of inputs and outputs to a data flowprocess when that process is decomposed to a lower

level – Ensures that the input and output data flows of the

parent DFD are maintained on the child DFD



0

Order

System

SALES

REP

CUSTOMER WAREHOUSE

BANK ACCOUNTING

Order

Order

Reject

Notice

Picking

List

CompletedOrder

Payment Invoice

Commission BankDeposit

CashReceipts

Entry

Level-0 DFD of

Order System



1.0

FillOrder

2.0

Create

Invoice

3.0

Apply

Payment

SALES

REPBANK ACCOUNTING

CUSTOMER WAREHOUSE

Order

Order

Reject

Notice

Picking List

Accounts

ReceivableD1

Invoice

Invoice

Invoice

DetailPayment

Detail

Payment

Commission Bank Deposit Cash Receipts Entry

Completed

Order

Level-1 DFD of

Order System



Entity Relationship Diagram (ERD)



DFD and ERD

DFD ERD

Process/

Relationship

Data store/

Attribute

External Entity/

Entity



Data Dictionary

• It is the repository of various data flowsdefined in DFD. The DD states precisely

the structure of each data flow in DFD. DD

stores details description of all elements thatcompose the DFD such as data flows, data

stores, processes.



Why DD is important

• To manage details in large system

• To communicate common meaning for all

system elements

• To document features of the system

• To analyse the system characteristics

• To locate errors and omissions in the system



Data dictionary entries

Name Description Type Date

has-labels1:N relation between entities of type Node or Link and entities of type Label. Relation 5.10.1998

LabelHolds structured or unstructured informationabout nodes or links. Labels are represented byan icon (which can be a transparent box) andassociated text.

Entity 8.12.1998

Link A 1:1 relation between design entitiesrepresented as nodes. Links are typed and maybe named.

Relation 8.12.1998

name (label)Each label has a name which identifies the typeof label. The name must be unique within theset of label types used in a design.

Attribute 8.12.1998

name (node)Each node has a name which must be uniquewithin a design. The name may be up to 64

characters long.

Attribute 15.11.1998

Data dictionaries are lists of all of the names used in thesystem models.

Descriptions of the entities, relationships and attributes arealso included



State machine models

• State Machine models the behaviour of thesystem in response to external and internalevents

• They show the system’s responses to stimuli soare often used for modelling real-time systems

• State machine models show system states as

nodes and events as arcs between these nodes.When an event occurs, the system moves fromone state to another

• Statecharts are an integral part of the UML



Microwave oven modelFull power

Enabled

do: operateoven

Full

power

Half power

Half power

Full power

Number

Timer Door open

Door closed

Door closed

Door

open

Start

do: set power = 600

Half power

do: set power = 300

Set time

do: get number exit: set time

Disabled

Operation

Timer

Cancel

Waiting

do: displaytime

Waiting

do: display

time

do: display'Ready'

do: display'Waiting'

State machine model doesnot show flow of data

within the system



Microwave oven stimuli

Stimulus DescriptionHalf power The user has pressed the half power buttonFull power The user has pressed the full power button

Timer The user has pressed one of the timer buttonsNumber The user has pressed a numeric keyDoor open The oven door switch is not closedDoor closed The oven door switch is closedStart The user has pressed the start buttonCancel The user has pressed the cancel button



Microwave oven state description

State Description

Waiting The oven is waiting for input. The display shows the current time.

Half power The oven power is set to 300 watts. The display shows ÔHalf powerÕ.

Full power The oven power is set to 600 watts. The display shows ÔFull powerÕ.

Set time The cooking time is s et to the userÕs input value. The display shows the cooking time

selected and is updated as the time is set.

Disabled Oven operation is disabled for safety. Interior oven light is on. Display shows ÔNot

readyÕ.

Enabled Oven operation is enabled. Interior oven light is off . Display shows ÔReady to cook Õ.

Operation Oven in operation. Interior oven light is on. Display shows the timer countdown. On

completion of cooking, the buzzer is sounded for 5 s econds. Oven light is on. Display

shows ÔCooking completeÕ while buzzer is sounding.



Statecharts

• Allow the decomposition of a model into sub-models (see a figure)• A brief description of the actions is included following the ‘do’ in each

state

• Can be complemented by tables describing the states and the stimuli

Cook

do: rungenerator

Done

do: buzzer on

for 5 secs.

Waiting

Alarm

do: displayevent

do: check status

Checking

Turntablefault

Emitter fault

Disabled

OK

Timeout

TimeOperation

Door open

Cancel



Finite state machinesFinite State Machines (FSM), also known as

Finite State Automata (FSA)

are models of the behaviours of a system or a

complex object, with a limited number of defined conditions or modes, where mode transitionschange with circumstance.



Finite state machines - Definition

A model of computation consisting of – a set of states,

– a start state,

– an input alphabet , and

– a transition function that maps input symbols and current states to a next

state

• Computation begins in the start state with an input string. Itchanges to new states depending on the transition function.– states define behaviour and may produce actions

– state transitions are movement from one state to another

– rules or conditions must be met to allow a state transition

– input events are either externally or internally generated, which may possibly trigger rules and lead to state transitions



Variants of FSMs

• There are many variants, for instance,

– machines having actions (outputs) associated with

transitions ( Mealy machine) or states ( Moore

machine),

– multiple start states,

– transitions conditioned on no input symbol (a null) or

more than one transition for a given symbol and state

( nondeterministic finite state machine),

– one or more states designated as accepting states

( recognizer), etc.



Finite State Machines with Output (Mealy and

Moore Machines)

• Finite automata are like computers in that

they receive input and process the input by

changing states. The only output that wehave seen finite automata produce so far is a

yes/no at the end of processing.

• We will now look at two models of finiteautomata that produce more output than a

yes/no.



Moore machine• Basically a Moore machine is just

a FA with two extras.

1. It has TWO alphabets, an input and output alphabet.

2. It has an output letter associated with each state. The

machine writes the appropriate output letter as it enters each

state.

The output produced by the machine contains a 1 for each

occurrence of the substring aab found in the input string.

This machine might be

considered as a

"counting" machine.



Mealy machine• Mealy Machines are exactly as powerful as Moore machines

– (we can implement any Mealy machine using a Moore machine, and viceversa).

• However, Mealy machines move the output function from the

state to the transition. This turns out to be easier to deal with inpractice, making Mealy machines more practical.



A Mealy machine produces output on a transition

instead of on entry into a state.

Transitions are labelled i/o where – i is a character in the input alphabet and

– o is a character in the output alphabet.

• Mealy machine are complete in the sense that there is atransition for each character in the input alphabet leavingevery state.

• There are no accept states in a Mealy machine because it isnot a language recogniser, it is an output producer. Itsoutput will be the same length as its input.

The following Mealy machine takes the one's

complement of its binary input. In other words, it

flips each digit from a 0 to a 1 or from a 1 to a 0.



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