Specification Techniques

System models are abstract descriptions of systems whose requirements are being analyzed

Objectives To explain why specification modeling techniques

help discover problems in system requirements To describe

– Behavioural modeling (Finite State Machines, Petri-nets),

– Data modeling and – Object modeling (Unified Modeling Language,

UML)

Formal Specification - Techniques for the unambiguous specification of software

Objectives: To explain why formal specification techniques

help discover problems in system requirements To describe the use of

– algebraic techniques (for interface specification) and

– model-based techniques (for behavioural specification)

System Modeling

System modeling 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

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 organizational concerns may affect the decision on where to position system boundaries

Architectural models show a system and its relationship with other systems

The Context of an ATM System

Auto-tellersystem

Securitysystem

Maintenancesystem

Accountdatabase

Usagedatabase

Branchaccounting

system

Branchcountersystem

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 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.

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

Software Design Semantic ModelDesign

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

Data dictionaries are lists of all of the names used in the system modelsDescriptions of the entities, relationships and attributes are also included

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

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 recognized as a difficult process requiring a deep understanding of the application domain

Object classes reflecting domain entities are reusable across systems

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

modeling 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 generalization and is shown ‘upwards’ rather than ‘downwards’ in a hierarchy

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 Flow Diagrams

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

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

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

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 modeling 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

State charts are an integral part of the UML

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

Microwave Oven Stimuli

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

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.

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.

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 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.

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 recognizer, 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.

State Charts 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

Cookdo: 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.

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

TransitionPlace

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.

The Classical Petri Net Model A Petri net is a network composed of places ( ) and transitions ( ).

t2

p1

p2

p3

p4t3

t1

Connections are directed and between a place and a transition.

Tokens ( ) are the dynamic objects.

The state of a Petri net is determined by the distribution of

tokens over the places.

Transition t1 has three input places (p1, p2 and p3)and two output places (p3 and p4).Place p3 is both an input and an output place of t1.

p1

p2

p3

p4t1

Enabling ConditionTransitions are the active components and places and

tokens are passive.A transition is enabled if each of the input placescontains tokens.

t1 t2

Transition t1 is not enabled, transition t2 is enabled.

FiringAn enabled transition may fire.

Firing corresponds to consuming tokens from the inputplaces and producing tokens for the output places.

t2t2

Firing is atomic.

Example

Non-Determinism

Two transitions fight for the same token: conflict.Even if there are two tokens, there is still aconflict.

t1

t2

A Collection of Primitive Structures that occur in Real Systems

High-Level Petri Nets The classical Petri net was invented by Carl Adam

Petri in 1962. Since then a lot of research has been conducted (>10,000 publications).

Since the 80-ties the practical use is increasing because of the introduction of high-level Petri nets and the availability of many tools.

High-level Petri nets are Petri nets extended with– color (for the modeling of attributes)– time (for performance analysis)– hierarchy (for the structuring of models, DFD's)

Modeling

States of a process are modeled by tokens in places andstate transitions leading from one state to another aremodeled by transitions.

Tokens represent objects (humans, goods, machines), information, conditions or states of objects.

Places represent buffers, channels, geographical locations, conditions or states.

Transitions represent events, transformations or transportations.

Example: Traffic Light

rg

red

yellow

green

yr

gy

Current StateThe configuration of tokens over the places.

Reachable StateA state reachable form the current state by firing a sequence of enabled transitions.

Dead StateA state where no transition is enabled.

Some Definitions

blackred

bbrr

br

High-Level Petri Nets

In practice the classical Petri net is not very useful: The Petri net becomes too large and too complex. It takes too much time to model a given situation. It is not possible to handle time and data.

Therefore, we use high-level Petri nets, i.e. Petri netsextended with: color time hierarchy

To explain the three extensions we use thefollowing example of a hairdresser's saloon.

start

waiting

finish

busy

free

ready

client waiting

hairdresser ready to begin

Note how easy it is to model the situation with multiple hairdressers.

The Extension with Color

A token often represents an object having allkinds of attributes.Therefore, each token has a value (color) whichrefers to specific features of the object modeled bythe token.

start

waiting

finish

busy

free

ready

name: Harryage: 28experience: 2

name: Sallyage: 28hairtype: BL

Each transition has an (in)formalspecification which specifies: the number of tokens to be produced, the values of these tokens, and (optionally) a precondition.

The complexity is divided over the network andthe values of tokens.

This results in a compact, manageable and naturalprocess description.

Examples

c := a+ba

b c

+

b := -a

b-a

if a> 0then b:= aelse c:=afi

a b

c

select

a >=0 | b := a

bsqrta

Extra Credit Exercise:calculate |a+b| using these building blocks

The Extension with Time

For performance analysis we need to modeldurations, delays, etc.Therefore, each token has a timestamp andtransitions determine the delay of a producedtoken.

start

waiting

finish

busy

free

ready

0

1

3 9

=3

=0=0

The Extension with Hierarchy A mechanism to structure complex Petri nets comparable to DFD's.

A subnet is a net composed out of places, transitions and subnets.

waiting ready

h1

h2

h3

start finishbusy

free

Key Points Modeling specification complements informal

requirements elicitation techniques. Model specifications can be precise and

unambiguous, but generally depend on interpretation of inputs/output. They reduce areas of doubt in a specification.

More formal models, such as FSM or Petri nets forces an analysis of the system requirements at an early stage. Correcting errors at this stage is cheaper than modifying a system during design.