System Models Hoang Huu Hanh, Hue University hanh-at-hueuni.edu.vn Lecture 6 & 7.
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Transcript of System Models Hoang Huu Hanh, Hue University hanh-at-hueuni.edu.vn Lecture 6 & 7.
System Models
Hoang Huu Hanh, Hue Universityhanh-at-hueuni.edu.vn
Lecture 6 & 7
System Models2
System models are abstract descriptions of systems whose requirements are being analysed
Objectives To explain why the context of a system should
be modelled as part of the RE process To describe
Behavioural modelling (FSM, Petri-nets), Data modelling and Object modelling (Unified Modeling Language,
UML)
System Models3
System modelling System modelling helps the analyst to
understand the functionality of the system and models are used to communicate with customers
Different models present the system from different perspectives External perspective showing the system’s
context or environment Behavioural perspective showing the behaviour
of the system Structural perspective showing the system or
data architecture
System models weaknesses They do not model non-functional system
requirements They do not usually include information
about whether 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
4 System Models
System Models5
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
1. 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 system and its relationship with other systems
6 System Models
The context of an ATM system
Auto-tellersystem
Securitysystem
Maintenancesystem
Accountdatabase
Usagedatabase
Branchaccounting
system
Branchcountersystem
7 System Models
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
8 System Models
Equipment procurement process
System Models9
Get costestimates
Acceptdelivery ofequipment
Checkdelivered
items
Validatespecification
Specifyequipmentrequired
Choosesupplier
Placeequipment
order
Installequipment
Findsuppliers
Supplierdatabase
Acceptdelivered
equipment
Equipmentdatabase
Equipmentspec.
Checkedspec.
Deliverynote
Deliverynote
Ordernotification
Installationinstructions
Installationacceptance
Equipmentdetails
Checked andsigned order form
Orderdetails +
Blank orderform
Spec. +supplier +estimate
Supplier listEquipment
spec.
2 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
10 System Models
2.1 Data-processing models
Data flow diagrams are used to model the system’s data processing
These show the processing steps as data flows through a system
IMPORTANT part of many analysis methods
Simple and intuitive notation that customers can understand
Show end-to-end processing of data
11 System Models
Order processing DFD
Completeorder form
Orderdetails +
blankorder form
Valida teorder
Recordorder
Send tosupplier
Adjustavailablebudget
Budgetfile
Ordersfile
Completedorder form
Signedorder form
Signedorder form
Checked andsigned order
+ ordernotification
Orderamount
+ accountdetails
Signedorder form
Orderdetails
12 System Models
Data flow diagrams DFDs model the system from a functional
perspective Tracking and documenting how the data
associated with a process is helpful to develop an overall understanding of the system
Data flow diagrams may also be used in showing the data exchange between a system and other systems in its environment
13 System Models
2.2 State machine models
State Machine models the behaviour of the system in response to external and internal events
They show the system’s responses to stimuli so are 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 from one state to another
Statecharts are an integral part of the UML
14 System Models
Microwave oven model
Full power
Enabled
do: operateoven
Fullpower
Halfpower
Halfpower
Fullpower
Number
TimerDooropen
Doorclosed
Doorclosed
Dooropen
Start
do: set power = 600
Half powerdo: set power = 300
Set time
do: get numberexit: set time
Disabled
Operation
Timer
Cancel
Waiting
do: display time
Waiting
do: display time
do: display 'Ready'
do: display 'Waiting'
State machine model does not show flow of data within the system
15 System Models
Microwave oven stimuli
Stimulus DescriptionHalf power The user has pressed the half power buttonFull power The user has pressed the full power buttonTimer 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
16 System Models
Finite state machines
Finite 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 transitions change with circumstance.
17 System Models
System Models18
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. It changes 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.
19 System Models
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 we have seen finite automata produce so far is a yes/no at the end of processing.
We will now look at two models of finite automata that produce more output than a yes/no.
20 System Models
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.
21 System Models
Mealy machine Mealy Machines are exactly as powerful as Moore
machines (we can implement any Mealy machine using a Moore
machine, and vice versa). However, Mealy machines move the output function
from the state to the transition. This turns out to be easier to deal with in practice, making Mealy machines more practical.
22 System Models
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 a transition for each character in the input alphabet leaving every state.
There are no accept states in a Mealy machine because it is not a language recogniser, it is an output producer. Its output 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.
23 System Models
System Models24
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 stimuliCook
do: run generator
Done
do: buzzer on for 5 secs.
Waiting
Alarm
do: display event
do: checkstatus
Checking
Turntablefault
Emitterfault
Disabled
OK
Timeout
TimeOperation
Dooropen
Cancel
Petri Nets Model
Petri Nets were developed originally by Carl Adam Petri, and were the subject of his dissertation in 1962.
Since then, Petri Nets and their concepts have been extended, developed, and applied in a variety of areas.
While the mathematical properties of Petri Nets are interesting and useful, the beginner will find that a good approach is to learn to model systems by constructing them graphically.
25 System Models
The Basics
A Petri Net is a collection of directed arcs connecting places and transitions.
Places may hold tokens. The state or marking of a net is
its assignment of tokens to places.
Place with token
P1
P2
T1
Arc with capacity 1
Transition
Place
26 System Models
Capacity Arcs have capacity 1 by default; if other
than 1, the capacity is marked on the arc. Places have infinite capacity by default. Transitions have no capacity, and cannot
store tokens at all.
Arcs can only connect places to transitions and vice versa.
A few other features and considerations will be added as we need them.
27 System Models
Enabled transitions and firing A transition is enabled when the number of
tokens in each of its input places is at least equal to the arc weight going from the place to the transition.
An enabled transition may fire at any time.
28 System Models
When arcs have different weights… When fired, the tokens in the input places are
moved to output places, according to arc weights and place capacities.
This results in a new marking of the net, a state description of all places.
29 System Models
A collection of primitive structures that occur in real systems
30 System Models
3. Semantic data models
Used to describe the logical structure of data processed by the system
Entity-relation-attribute model sets out the entities in the system, the relationships between these entities and the entity attributes
Widely used in database design. Can readily be implemented using relational databases
No specific notation provided in the UML but objects and associations can be used
31 System Models
Software design semantic model
System Models32
Design
namedescriptionC-dateM-date
Link
nametype
Node
nametype
links
has-links
12
1 n
Label
nametexticon
has-labelshas-labels
1
n
1
n
has-linkshas-nodes is-a
1
n
1
n1
1
Data dictionary entries
Name Description Type Date has-labels
1:N relation between entities of type Node or Link and entities of type Label.
Relation
5.10.1998
Label
Holds structured or unstructured information about nodes or links. Labels are represented by an icon (which can be a transparent box) and associated text.
Entity
8.12.1998
Link
A 1:1 relation between design entities represented as nodes. Links are typed and may be named.
Relation
8.12.1998
name (label)
Each label has a name which identifies the type of label. The name must be unique within the set of label types used in a design.
Attribute
8.12.1998
name (node)
Each node has a name which must be unique within 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 the system models. Descriptions of the entities, relationships and attributes are also included
33 System Models
4. Object models
Object models describe the system in terms of object classes
An object class is an abstraction over a set of objects with common attributes and the services (operations) provided by each object
Various object models may be produced Inheritance models Aggregation models Interaction models
34 System Models
System Models35
Object models
Natural ways of reflecting the real-world entities manipulated by the system
More abstract entities are more difficult to model using this approach
Object class identification is recognised as a difficult process requiring a deep understanding of the application domain
Object classes reflecting domain entities are reusable across systems
System Models36
The Unified Modeling Language Devised by the developers of widely used
object-oriented analysis and design methods Has become an effective standard for object-
oriented modelling Notation
Object classes are rectangles with the name at the top, attributes in the middle section and operations in the bottom section
Relationships between object classes (known as associations) are shown as lines linking objects
Inheritance is referred to as generalisation and is shown ‘upwards’ rather than ‘downwards’ in a hierarchy