Software Engineering unit 3

Abhimanyu Mishra(CSE) JETGI 1

SOFTWARE ENGINEERINGUNIT-3

ABHIMANYU MISHRAASSISTANT PROF.(CSE)

JETGI

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Basic concept of software design. Architectural design. Low level designs. Design strategies. Software Measurement and Metrics. Function point based measure. Cyclomatic complexity Measure. Control flow diagram.

CONTENTS

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Basic design principles that enable the software engineering to navigate the design process suggest a set of principles for software design, which have been adapted and extended in following list:- Free from the suffer from “tunnel vision”. The design should be traceable to the analysis model. The design should not repeat the same thing. The design should “minimize the intellectual distance” between the software and

the problem as it exists in real world. The design should exhibit uniformly and integration. The design should be accessed for quality as it is being created. The design should be able to handle all unusual circumstances. Design is not coding and coding is not design.

Software Design :

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DESIGN PROCESS

The general design process shown above characterizes many design disciplines, including building architecture, graphic design, various branches of engineering, and software design. The software design process can be decomposed mainly into the following three level of design:

Interface level Architecture level Detailed level

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Interface design- Interface design is the specification of the interaction between a system and its environment. This phase proceed at high level.

Architectural design- The objective of architectural design is modelling of overall software structure by representing components interface dependency and relationship and interaction.

Detailed design- Detailed design is the specification of the internal elements of all major system components, their structure, properties, relationship and often their algorithm and data structures.

Levels of design:

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Abhimanyu Mishra(CSE) JETGI 624/12/16

Software

Super class ordinate

Peer user

Sub routineUser

Actor

Fig- Architectural design


“A modular system consists of well-define, manageable units with well define interfaces among the units. The modularization of problem partitioning, sometimes called problem decomposition”.

Desirable properties of modular system include:

•Each module is a well defined subsystem that is potentially useful in other application.

• Each module has a single, well define purpose.

•Modules can be separately compiled and stored in a library.

•Module can use other modules.

•Module should be easier to use than to build.

•Module should be simpler from the outside than from the inside.

Low level design- Modularization:

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Principle of Modularity:

Certain principles must be followed to ensure proper modularity:• Linguistic modular units.• Few interface• Small interface(Weak coupling)• Explicit interface• Information hiding

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Structural Chart

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For a function oriented design, the design can be represented graphically, by structural charts. The structure of a program is made up of the modules of that program together with the interconnections between modules.

As an example consider the structure of the following program, whose structure is:Main(){ int sum, n N, a[MAX],j; Readnums (a,&N); Sort(a ,N); Scanf (&n); Sum= Add_n (a,n); printf(Sum);} Readnums (a,N) int a[], *N;{}


SumMain

Readnums Sort Add_n

Switch

a,nA,n a

x,yx,y

Fig- The structure chart of the sort program


Coupling and Cohesion-

Coupling is the measure of the degree of independence between modules. Two modules are considered as independence modules if they individually can work effectively without help to another module. Two modules that are strongly connected, highly dependent on each other mean one can’t work properly in absence of another is called highly coupled module.

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Uncoupled modules Highly-coupled modules


COHESION MEASURES:

Cohesion is the measure of functional relatedness elements within a single module. When dividing a system into modules, it must be ensure that the activities within the module are tightly bound to one another. It can be viewed as opposite of coupling. The level of cohesion go from good to bad i.e. the first from of cohesion described below is the best form of cohesion and the last form of cohesion is the worst form of cohesion.

“In functional independent module we have various advantages such as module reusability, reducing the complexity of the design and reducing the errors”.


Types of cohesion module:

Functional cohesion- All activities in the module are functionally related or they are performing a similar function such as interest calculation, tax calculation, etc.

Sequential cohesion- In this scheme of cohesion, modules are divided into a series of activities such that output of one module becomes the input to the next module and chain continues.

Communicational cohesion- This form of cohesion relates to a situation where all modules share common data.

Procedural cohesion- In this scheme, activities share the same procedural implementation.

Temporal cohesion- In this type of module cohesion, activities occurring in the same period are grouped together.

Logical cohesion- In logical cohesion, activities belonging to the same category.

Coincidental cohesion- There is no criteria of module.

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FUNCTION ORIENTED DESIGN:

Function oriented design is an approach to software design where the design is decomposed into a set of interacting units where each unit has a clearly defined function. Thus, a system is design from a functional view point.

One of the best-known advocates of this method is NIKLAUS width, the creator of PASCAL and a number of other language.

For a function oriented design, the following can represent the design graphically or mathematically: Data flow diagram Data dictionaries Structure charts Pseudo-code


Software Measurement & Metrics:

• Software measurement is a new way of managing software development. Software measurements once an obscure and mysterious specialty has become essential to good software engineering.

• Measurement enables software managers to assess software quality, improve the software process and assist in planning, tracking, and control of a software project.

• A prediction system consists of a mathematical model together with a set of prediction procedures for determining unknown parameters and interpreting result.

cost = s*c/dWhere s is distance , c is the cost and d is average distance.

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SOFTWARE METRICS:

Software metrics can be defined as “It is a continuous application of measurement based techniques to the software development process and its products to supply meaningful and timely management information, together with the use of those techniques to improve all aspects of the management process.Categories of metrics:i. Product metricsii. Process metricsiii. Projects metrics Areas of application- The most established area of software metric is cost and size estimation techniques. There are many proprietary packages in the market that provide estimates of software system size, cost to develop a system, and the duration of the development or enhancement of the project.


HALESTEAD’S SOFTWARE SCIENCE:

In the early 1970s, the “Late Professor Maurice Halestead and his co-workers” at Purdue university developed the software science family of measurement.Tokens are classified as either operators and operands. All software science measures are function of the counts of these tokens. It can be classified as: Program length- n= n1+n2 n: Vocabulary of a program n1: Number of unique operators n2: Number of unique operator Program Volume- V= N*log2n The unit of volume is the common units for size “bits” . It is actual size of program if a uniform binary encoding for the vocabulary is used.

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Advantages of Halestead’s software matrices: Do not require indepth analysis of programming structure. Predict rate of error. Predict maintenance efforts. Useful in scheduling. Measures overall quality of programs. Simple to calculate. Can be used for any programming language.

Disadvantages of Halestead’s software matrices: It depends on complete code. It has little or no use for predictive estimation model


Functional point based measures:

Alan Albercht while working for IBM, recognized the problem in size measurement in the 1970s, and developed a technique which he called “Functional Point Analysis”.It measures functionality from the users point of view, that is, on the basis of what the user request and receives in return. Therefore, it deals with the functionality being delivered, and not with the line of code.

“The principle of Alberncht’s function point analysis is that a system is decomposed into functional units”. Inputs: information entering the system. Outputs: information leaving the system. Enquiries: request for instant access to information. Internal logical files: informations held within the system. External interface files: information held by other system that is used by the

system being analyzed.


ILFSystem

User

Other applicati

onUser

Inquiries

EIFInputs

Fig- Functional point analysis


6

4

Functional unitsWeighting factors

Low Average High

External input 3 4

External output 5 7

External inquiries 3 4 6

Internal Logical Files 7 10 15

External Interface Files 5 7 10

Fig- The 5 functional unit are ranked according to their complexity i.e. low, average, high using a set of prescriptive standards.


Advantages of functional point analysis: It is not restricted to code. Language independent. More accurate than estimated LOC>

Disadvantages of functional point analysis: Subjective counting. Hard to automate and difficult to compute. Ignores quality of output. Oriented to traditional data processing application. Effort prediction using the unadjusted function count is

often no worse then when the TCF is added.


CYCLOMATIC COMPLEXITY:

The Cyclomatic complexity is also known as structural complexity because it gives internal view of the code. This approach is used to find the number of independent path through a program. This provide us the upper bound for the number of tests that must be conducted to ensure that all statements have been executed at least once and every condition has been executed on its true and false side.

McCabe’s Cyclomatic metric, V(G) of a graph G with n vertices, e edges, and P connected components is V(G) = e-n+2p V(G) =pi + 1 V(G) = number of region


Fig- Example of Cyclomatic complexity

V(G)= 8-6+2 4