Smqa unit ii

CHAPTER 2

MEASURING INTERNAL
PRODUCT ATTRIBUTES

2.1 Aspects of Software Size
 Effort
 Productivity
 Cost

Software size can be outlined using three attributes:
 Length
 Functionality
 Complexity

Mr. M. E. Patil
S.S.B.T COET, Bambhori

2.2 Length
 Code
 Traditional code measures
 Dealing with non-textual or external code
 Specification and designs

2.3 Functionality
 Function Points
Total
Measurement Parameters Count Weight Factor
Simple Ave Complex
Number of Inputs * 3 4 6
Number of User Outputs * 4 5 7
Number of User Inquiries * 3 4 6
Number of Files * 7 10 15
Number of External Interfaces * 5 7 10
Count-Total
Mr. M. E. Patil

FP = count-total * [ 0 . 65  0 . 01 * SUM ( Fi )]
Fi are complexity adjustment values founded on the
answers to the questions in the table below.
Fi
1 Does the system require reliable back up and recovery?
2 Are data communication required
3 Are there distributed processing functions?
4 Is performance critical
5 Will the system run in an existing, heavily utilized operating system

6 Does the system require on-line entry?
7 Does the on-line data entry require the input transaction to be built over multiple screen or
operations?
8 Are the master files updated on-line?
9 Are the inputs, outputs, files, or inquiries complex?
10 Is the internal processing complex?
11 Is the code designed to be reusable
12 Are the conversion and installation included in the design?

13 Is the system designed for multiple installations in different organizations?

14 Is the application designed to facilitate change and ease by the user?
Mr. M. E. Patil
Total

FP
Productivi ty 
person - months
quality
Quality 
FP
£
Cost 
FP
pages of documentation
Documentation 
FP

Mr. M. E. Patil

COCOMO 2.0
• Alternatives to lines of code as a size input for
a revised COCOMO, Boehm chose function
points for use when the system is totally
specified.
• They required a size measure for earlier in the
development stage, when feasibility is being
considered and prototypes produced.
• They choose object points for this early size
measure in COCOMO 2.0.

Mr. M. E. Patil

• To calculate object points, an initial size
measure is produced by counting the number
of screens, reports and third-generation
language elements that are to be included in
the application.
• Each object is classified as simple, medium or
difficult in a manner close to function points
(See table below).

Mr. M. E. Patil

 COCOMO 2.0
Table Object point complexity levels.
For Screens
Number and source of data tables
Number of views Total < 4 Total <8 Total 8+
contained (<2 server, (2-3 servers, (>3 server,
< 2 clients) 3-5 client) >5 client)
<3 Simple Simple Medium
3-7 Simple Medium Difficult
8+ Medium Difficult Difficult

For Reports
Number and source of data tables
Number of Total < 4 Total <8 Total 8+
sections contained (<2 server, (2-3 servers, (>3 server,
< 2 clients) 3-5 client) >5 client)
<3 Simple Simple Medium
3-7 Simple Medium Difficult
8+ Medium Difficult Difficult
Mr. M. E. Patil

Table Complexity weights for object points.

Object type Simple Medium Difficult

Screen 1 2 3

Report 2 5 8

3GL component - - 10

The reuse is taken in account.

New object points = (object points) x (100 – r)/100

Where r is the % of reused components
Mr. M. E. Patil

DeMarco’s Approach
• DeMarco produced a functionality measure
founded on his structured analysis and design
notation.
• These bang metrics included two measures:
– one for “function strong” systems, and
– another for “data strong” systems.
Bang may be defined formally and its computation
can be automated within CASE tools that support
the mehtodology

Mr. M. E. Patil

Function Strong
• The function bang measure is founded on the
number of functional primitives (the number
of lowest level bubbles) in a data-flow
diagram.
• The basic function-primitive count is weighted
based on the kind of functional primitive and
the number of data tokens used in the
primitive.

Mr. M. E. Patil

Data Strong
• The data bang measure is founded on the
number of entities in the entity-relationship
model.
• The basic entity count is weighted according
to the number of associations involving each
entity.
• These can be easily adapted for structured
analysis technique.

Mr. M. E. Patil

Complexity
• Complexity of problem:- Amount of resources
required for an optimal solution.
• Complexity of solution:- Resources needed for
implementation of a particular solution.
– Time complexity :- resource is computer time
– Space complexity:- resource is computer memory

Mr. M. E. Patil

• Measuring algorithmic efficiency:-
– Efficiency of a searching or sorting algorithm can
be measured as the function f(n) representing the
number of primitive operations required for an
input list of size n.

Mr. M. E. Patil

Big-O notations
• A precise mathematical formalism is called as
Big-O notation.
• It allows us to define an order relation on
functions.
• This permits us to define relation to
characterize efficiency.
• We derive it of a function f(n) by finding the
dominating term of f(n) an then ignoring
constant multiples.
Mr. M. E. Patil

• F(n) = 3 n2 + 2n +26
• The dominating term of f(n) is 3n2
• Ignore the constant 3 it will become n2
• Thus we can say that big-o n2 can be written
as @(n2)

Mr. M. E. Patil

Structure
• Types of structural measures

– Control flow structure
– Data-flow structure
– Data structure

Mr. M. E. Patil

Control flow
• It address the sequence in which instructions
are executed in a program.
• This aspect of structure reflects the iterative
and looping nature of program
• Simple size counts an instruction just once,
control flow makes more visible the fact that
an instruction maybe executed many times as
the program is actually rns.

Mr. M. E. Patil

Data flow
• It follows the trail of data item as it is created
or handled by the program.

• It trace the behavior of the data as it interacts
with the program.

Mr. M. E. Patil

Data Structure
• It is the organization of the data itself,
independent of program.
• The structure of data tells us about the
difficulty involved in writing programs to
handle the data.

Mr. M. E. Patil

Control-Flow Structure
• The control flow measures are usually
modeled with directed graph.
• Each node (or point) corresponds to a
program statement
• Each arc (or directed edge) indicates the flow
of control from one statement to another.
• We call these directed graphs control flow
graphs or flow graphs.
Mr. M. E. Patil

2.7 Control-Flow Structure
 Flow graphs-
10 INPUT P 10

20 Div = 2 20
30 Lim=INT(SQR(P)) 30
40 Flag = P/Div- INT(P/Div)
40
50 If Flag = 0 OR Div = Lim
THEN 80 50
60 Div = Div +1 80 60
70 GO TO 40
80 IF Flag <> 0 OR P>4 THEN
110 90
110
90 Print Div
100 GO TO 120
110 Print P 120
120 End

Mr. M. E. Patil

Flow graph model of structure
• Graph consists of set of points (or nodes) and
line segments (or edges).
• In a directed graph , each edge is assigned a
direction , indicated by a arrowhead on the
edge . This directed edge is called an arc.
• Arc is an ordered pair , <x , y> where x and y
(nodes) are the starting and ending point of
the arc.

Mr. M. E. Patil

• In-degree:- of the node is the number of arcs
arriving at the node
• Out-degree:- of the node is the number of
arcs that leave the node.
• A path is the sequence of consecutive
(directed ) edges, some of which may be
traversed more than once during sequence.
• Simple path is the one in which there are no
repeated edges.
Mr. M. E. Patil

• A Flowgraph is a directed graph in which two
nodes, the start node and the stop nodes,
obeys the properties:-
– Stop node has out degree zero
– And every node lies on the some path from the
start node to the stop node

Mr. M. E. Patil

Mr. M. E. Patil

Sequencing and Nesting
• There are only two operation available with
which we can build a new flow graph form the
old.

Mr. M. E. Patil

2.8 Modularity and Information Flow
Attributes
 Models of modularity and information flow

Main
Scores
Scores

eof

Read_Scores
Average
Scores
Average

Average

Calc_Av Print_Av

Mr. M. E. Patil

Module call-graph
A Module A calls B, C

C Module B calls D
B
Module C calls D, E

D E

 Global Modularity
M 1 = modules/procedures
M 2 = modules/variable

Mr. M. E. Patil

 Morphology

Size 12 nodes, 15 edges
Depth 3
Width 6
Edge-to-node ratio 1.25

Mr. M. E. Patil

 Coupling
 No coupling relation
 Data coupling relation
 Stamp coupling relation
 Control coupling relation
Common coupling relation
 Content coupling relation
 Cohesion
 Functional
 Sequential
 Communicational
 Procedural
number of modules having functional cohesion
 Temporal Cohesion ratio =
total number of modules
 Logical
 Coincidental

Mr. M. E. Patil

 Information Flow

a
Fan-out for the figure is 4.

b c d e

w x y
Fan-in is 3 for z.

z
Mr. M. E. Patil

 Henry and Kafura’s Metrics

length × (fan _ in × fan _ out )2

WC

FD CW DR

GDR RD FWS PW

DOC

Mr. M. E. Patil

Module Fan-in Fan-out [(fan-in)(fan- Length Complexity
out)]
WC 2 2 16 30 480
FD 2 2 16 11 176
CW 3 3 81 40 1080
DR 1 0 0 23 0
GDN 0 1 0 14 0
RD 2 1 4 28 112
FWS 1 1 1 46 46
PW 1 1 1 29 29

 Shepperd’s Metric

IF4 m = ( fan _ inm × fan _ out m ) 2
IF4=Sum( IF4 m )

Mr. M. E. Patil

 Moses, Farrow and Smith Metric

Information flow Advantages
• Measures total level of information flow between the modules of a system.
• Evaluates potential design changes.
• Allows the identification of design problems such as modules with a large amount of
fan-in and fan-out.
• Can be produced in the design stage
• Takes into account data-driven programs.
• Address issues such as complexity and cohesion.
• Suitable for identifying software that exhibits maintenance problems and may be
error prone and unreliable.
• Appropriate for identifying lack of cohesion, stress points and missing levels of
design abstraction.
• Provides motivation for their use in the prediction of cohesion.

Information flow disadvantages
• All the flows have equal weighting.
• If there is no external interactions can give misleading values.
• Underestimates local and global information flows

Mr. M. E. Patil

 Object-Oriented Metrics

Metric 1: weighted methods per class (WMC)
Metric 2: depth of inheritance tree (DIT)
Metric 3: number of children (NOC)
Metric 4: coupling between object classes (CBO)
Metric 5: response of the class (RFC)
Metric 6: lack of cohesion metric (LCOM)

2.9 Data Structures
2.10 Difficulties with General “Complexity”
Measures
2.11 Measuring External Product Attribute

Mr. M. E. Patil

 Modeling Software Quality

Device Independence
Portability
Completeness

Reliability Accuracy

Consistency
As is utility Efficiency

Device efficiency

Human Engineering
Accessibility

General
Communicativeness
Utility
Testability
Metrics
Structuredness

Understandability Self descriptiveness
Maintainability

Conciseness
Modifiability

Legibility

Augmentability

Primary uses Intermediate
Primitive Constructs
Construction
Mr. M. E. Patil

 Early model
 The ISO 9126 standard quality model
Functionality
Reliability
Efficiency
Usability
Maintainability
Portability

Mr. M. E. Patil

Managerial Requirement definition
Stated or implied needs requirements
ISO 9126 & other technical info

Quality
Requirement
definition Metric Rating level Assessment
selection definition criteria Preparation
definition

Software
development Products

Measured
Measurement
value

Rating
Related
value
Result
Assessment (accepted or
unaccepted)

Mr. M. E. Patil

 Measuring Aspects of Quality
ET
Portabilit y = 1 -
ER
ET – resources needed to move the system to the
target environment
ER – measure of the resources needed to produce the
system for the resident environment.

 Defects-based quality measures
number of known defects
defect density =
product size

 Usability
Usability of a software product is the extent to which the product is convenient and
practical to use.
 Entry level
 Learnability
 Handling ability
Mr. M. E. Patil

quantity * quality
task effectiveness = %
100
effectiven ess
temporal effectiven ess =
task time

task time - unproductive time
productive period = 100%
task time

 Reusability

 Reliability
Probability of failure on demand
Rate of Fault Occurrence
Mean time to failure
Availability

Mr. M. E. Patil

 Maintainability

 Understandability

Mr. M. E. Patil

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