Analysis modelling

 Objectives of analysis model
◦ To describe what the customer require
◦ To establish a basis for the creation of a software
design
◦ To define a set of requirements that can be
validated once the software is built.
Uses a combination of text and diagrams to show
requirement

 What is a model?
◦ a model is a simplification of reality

 Why do we model?
◦ we build models so that we can better understand
the system we are developing
◦ we build models of complex systems because we
cannot comprehend such a system in its entirety

 Shows 3 aspects of a software :
2. Data Modeling
3. Functional Modeling
4. Behavioral Modeling

Data Dictionary – A repository that contains description
of all data objects consumed or produced by the
software.
3 diagrams surround the core –
1. ERD (Entity relationship diagram) – Depicts
relationship between objects.
2. DFD (Data Flow diagram)- Shows how data is
transformed as they move through the
system.
3. STD (State transition diagram) – Shows how a
system behaves as a consequence of external
events.

Data Modeling :
2. What are the primary data objects?
3. What attributes describe the object?
4. What are the relationships between each
objects?
To answer these, data modeling methods make
use of ERD--
Defines all data that are entered, stored ,
transformed and produced within an
application.

Functional Modeling(DFD)

external entity

process

data flow

data store

Notations used

 A producer or consumer of data
 Example: person, device, system, sensor

 Data must always originate from somewhere, and must
always be sent to something

Notations used :

 A data transformer (changes input to output)
 Example: compute taxes, determine area, format report,
display graph

 Data must always be processed in some way to achieve
system function

 Notations used

 Data flows through a system, beginning as input and be
transformed into output

base
compute
area
triangle
height area

Data Store
 Data is often stored for later use

sensor #
sensor #, type,
look-up location, age
sensor
report required data
type,
location, age
sensor number

sensor data

Rules for drawing a DFD :
 All icons must be labeled with meaningful names
 The DFD evolves through a number of levels of detail
 Always begin with a context level diagram which
depicts the system as a single bubble (also called level
0)
 Always show external entities at level 0
 Always label data flow arrows
 Do not represent procedural logic
 DFD should be balanced.
 A data store cannot be connected either to another data
store or to an external entity.

Balancing of DFD
a p b
x P y level 0

a c p2
p1
f

p4 b
d 5
p3 e g
level 1

Extensions for real time systems.
• To accommodate analysis of real time

software, we use extensions to basic DFD
notations called Ward and Mellor Extensions.

Notations used
A data item that is input or output
from a process on a time
continous basis
A process that accepts control
input or output.

A control flow/event

A data store that stores control
information.

3. Behavioral modeling (State transition diagram)
STDs represent----
• Behavior of system by depicting it’s state.

• Events that cause system to change state.

• What actions are to be taken as a consequence of a

particular event?

 State—a set of observable circumstances that
characterizes the behavior of a system at a
given time
 State transition—the movement from one
state to another
 Event—an occurrence that causes the system
to exhibit some predictable form of behavior
 Action—process that occurs as a consequence
of making a transition

State Transition Diagram Notation

state

event causing transition
action that occurs

new state

Example
Consider an XYZ project which has a type of
sensor to measure air temperature.
The sensor continuously sends out one of the
three signals HIGH, NORMAL, LOW.
If the temperature signal is high then AC is
turned ON.
If it is low then heater is turned ON.
The air conditioner/ heater is turned OFF when
the temperature is NORMAL.

Example STD for a temperature NORMAL
sensor
є
HIGH HOFF

AON AOFF
LOW
NORMAL NORMAL
HON
AOFF HOFF

HOFF HON

AON AOFF

HIGH LOW
є є

Mechanics of structured analysis :
 Create an ER diagram.
 Create a DFD diagram.
 Create a control flow model

• Large class of applications are driven by events.
• Such applications require the use of control flow
modeling in addition to data flow modeling.

4. Create a control specification.
Represents the behavior of the system.
a. Contains a STD.
b. Contains a process activation table (PAT) which contains
which processes will be invoked when an event occurs.
Eg: Input events
Temp High 1 0 0
Temp Normal 0 1 0
Temp Low 0 0 1
Process Activation
ACOn Hoff 1 0 0
Hoff Aoff 0 1 0
ACOff Hon 0 0 1

5. Create a Process Specification(PSPEC)
• Used to describe all flow model processes that
appear at the final level of refinement.
• Contents of PSPEC can be a narrative text,
algorithm, table etc.

Process Specification (PSPEC) can be used to
specify the processing details implied by a
process within a DFD Check &
convert
pressure

PSPEC
If absolute tank pressure > max pressure
then
set above pressure to “true”;
else
set above pressure to “false”;
begin conversion algorithm x-01a;
compute converted pressure;
end
end if

• A repository of data in a system
• It enables to find answers to the following
questions.
DD contains 2 types of descriptions for the
data flowing through the system-
o Data elements
o Data structures

1. Data Element
The most fundamental unit of data.
Eg. Invoice no, Amount due etc.
Describing data elements
Data element
Description
Type
Length
Aliases
Range of values
Typical value
Other details

Eg. Data element : Employee no.
Description : Identifies each employee in the
organization
Type : Alphanumaric
Length : 7
Aliases : Empid
Range of values : NA
Typical value : AC41000
Other details : Employee no. includes a 5 digit no.
and department prefix.
Valid prefixes
AC Accounting
AD Advertising
RD Research and development

2. Data Structures
Set of data items that are related to each other.
Eg. Pay Cheque-- Date
Amount
Pay to
Account no.

4 types of relationship exists between
components of a data structure.
2. Sequence relationship.
Defines the set of data items that make up a
data structure.

eg. Student university record consists of
Name
First Name
Middle Name
Last name
Street Address
City
State
Telephone no.
Use the symbol -- +

2. Selection relationship
Represents either/or relationship.
i.e a choice of one item must be made from a set of 2/more
items.
Eg. Student data structure
eg. Student data structure
Name
Street Address
City
State
Telephone no.
and one of the following
Student No.
Social Security no.
Write options in [ ] , each option separated by I (vertical line)

3. Iteration relationship (Repetition)
Data elements composing the data structure are repeated zero/
one/more times.
Eg. Term registration data structure
Term
Year
Advisor
1 to 6 iteration of course
Course no.
Course name
Time
Day
Instructor
Notation : All iteration data elements are shown in { }n
n-no. of iterations.

4. Optional relationship
Elements which may or may not be included.
First Name
Middle Name
Last name
Where middle name could be optional
Notation - ( )

Eg :
Student data = Name + street address +city
+state +postal code + [Student No. I SS no]
+{Course no + Course name + time + day +
Instructor} + Term + year + advisor

Name =First Name + (Middle Name) + Last
name

Analysis modelling

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