Interface Effectiveness Testing Metric And Its Verification Computer Science Essay

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There exist a lot of metrics about web site Effectiveness but no one clearly measure the effectiveness of website interfaces. Website guidelines varies due to the diversity. We have develop a methodology to measure and improve the effectiveness of certain website interface. Propose metric /model, later use the deviation analysis and correlation analysis to represent the difference from the standard guidelines and an indication of weak areas in interface design.

Good interface designs can be reason for acceptance and rejection of any software. In this paper we are going to define a metrics. By Putting certain values in that ,anyone can determine ,how affective and usable the particular interface is. Later on we have case study from a software house and we put values from certain web sites and application software to determine the effectiveness of proposed metrics/model.

Currently metrics lack the …………………………………..

1.Introduction

The interface design should follow three basic principal, give the user control of interface, reduce user memory load and make the use interface consistent.[10]

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World wide web has unique interface that is different from normal graphical applications.

WWW introduces unique human-computer interface interaction. User move in hypertext environment surfing the unrelated site than stand alone software applications.[1]

There exist a number of website metric like interface complexity metric,Web Page Design Metrics.[6]in web page design metrics a certain automated tool count the number of …………………………….Similarly

Important Interface paradigms in interface complexity metrics of a software component S,are interface signature, Constraints, Packaging and Non-functional Properties. This define the overall capability of the component, such that:

CICM(S) = a Cs + b Cc + c Cg

where

Cs is the complexity contributed by interface signature, Cc is the complexity contributed by interface constraints, and Cg is the complexity contributed by interface configurations of the software component. a, b, and c are the respective coefficients for Cs, Cc and Cg, and are dependent on the nature of software component and the nature of its interfaces. But the problem with this approach is that it not generic and varies application to application[2]

2:Design Of Metric

The Webby organizer place web sites into 27 categories, including news, personal,finance, services, sports, fashion, and travel. We are taking only few according to the domain [6].

Business

Bs

Finance

Fi

Services

Sv

Sports

Sp

Fashion

Fh

Travel

Tv

Education

Ed

2.1 Critical web Component in Design

There are certain important and critical parameters in website design given in [3,4]

In following tables we have listed some of the important parameters to these particular parameters can be assigned according to the category ,and standard guidelines about importance of particular attribute and according to the of the website application.

Color

Color Selection

C1

Contrast

C2

Leading

C3

Kerning

C4

Typefaces

C5

Type weight

C6

All Capital Letters

C7

Layout and Style

Scrolling

LS1

Consistency

LS2

Alignment

LS3

Logos and Graphics

LS4

Animation

LS5

Water Marks

LS6

Emboss Logos

LS7

Balance of Type and Open Space

LS8

Hand Eye Coordination

LS9

Hard Coding

LS10

Navigational Bars and links

LS11

Data stamping

T1

All Tags

T2

Archieve old articals

T3

Search capability

T4

Page size and download speed.

T5

Tables & frames.

T6

Proprietary tags scripting.

T7

Language, reading level & terminology.

T8

User Customization Testing

Customization, software

UC1

peripheral upgrades.

UC2

Browser Customization

UC3

*more table can be form

Any parameter form the above table can be assigned specific weight. These weight can be variable and can vary according to the design n category of application.e.g.Some website category like education can not be as funky colors as some commercial business website.

2.1 Hierarchical Break Down of website can be view in a tree Graph

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For designing of Metic,we divide the web according to the following tree,n we make measurement of attribute and use it in metric

WEBSITE

Categories

Components

Metric Attributes

Website categories Cover the broad ranges. Now in proposed model we grade the each

Category with 100 Points. This 100 point is further divided into components according to the importance of that particular component in that category Later this Component weight is further subdivide into attribute according to importance of Particular attribute in that component. Importance and weight assignment is determined from standard guideline. Later On after tester/??? Will grade these components ,according to standard and we can use prescribe metric to determine ,How affective the particular interface is

This method will not only describe the effectiveness but also deviation statistics .

Games(100),Bussiness,…………………….,etc[Categories]

Color(40),LayOut,(30)…………….etc[Components,weight]

Color selection(30) ,Contrast(10),Leadin,…….up to ()[attribute,static weight]

2 Explanation of Experimental Design and Regression Analysis

Each category is carrying initially the 100 pints.100 is maximum Limit for the Effectiveness of any website. Now we can divide this maximum number 100 in different component like in Color, LayOut and Style ,Customization according to their category. Now each particular attribute is assigned a static weight according to the weight that it is inheriting. The developer of tester ll assign the particular number according to the guidelines .e.g. If color selection is according to standard then assign the number from 20.Condition for grading is that particular value to particular attribute should not exceed the Component weight.

Effectiveness Metric value =y=[(sum of all attributes values) is x % of Component]+

Sum of all the Component is x% of Category

So the website is (answer)% effective. Later on from obtained percentage we can determine the effectiveness category from table.

Category

weighted Percentage

Excellent

100-90

Good

90-80

Average

80-70

Fair

70- onward

=

The value obtained will represent the independent variable for that component and we ll use the regression analysis to determine the impact on our depend variable Effectivness .

Now we ll use the statical analysis technique of correlation to show the affect of these attributes on Component and website. And later we will use the deviation analysis and regression analysis to show the impact.

We know the deviation of data set can be form by using this formula.

Let X be a random variable with mean value μ:

\operatorname{E}[X] = \mu.\,\!

Here the operator E denotes the average or expected value of X. Then the standard deviation of X is the quantity

\sigma = \sqrt{\operatorname{E}\left[(X - \mu)^2\right]}.[7]

Imaginary Case Study 1

Category:Business

Compnent:Color

Attribute:xxx,xxx,xxx,xxxx,xxxx,xxxx,

The obtain value for color attributes are

2,\;4,\;4,\;4,\;5,\;5,\;7,\;9.

There are eight data points in total, with a mean (or average) value of 5:

\frac{2 + 4 + 4 + 4 + 5 + 5 + 7 + 9}{8} = 5.

To calculate the population Color deviation, first compute the difference of each data point from the mean, and square the result:

\begin{array}{ll} (2-5)^2 = (-3)^2 = 9 & (5-5)^2 = 0^2 = 0 \\ (4-5)^2 = (-1)^2 = 1 & (5-5)^2 = 0^2 = 0 \\ (4-5)^2 = (-1)^2 = 1 & (7-5)^2 = 2^2 = 4 \\ (4-5)^2 = (-1)^2 = 1 & (9-5)^2 = 4^2 = 16 \end{array}

Next divide the sum of these values by the number of values and take the square root to give the standard deviation:

\sqrt{\frac{9+1+1+1+0+0+4+16}{8}} = 2.

So deviation for average Color component is 2

So deviation for average Style& Layout component is 10

So deviation for average Customization component is 18

Imaginary Case Study 2

Category:Education

Compnent:Color,LayOut,

Attribute:xxx,xxx,xxx,xxxx,xxxx,xxxx

Statistical Validation of Proposed Metric through regression analysis(in process from attribute to singly variable effectivness)

Multiple regression analysis is used to predict the values of one dependent variable (DV) Y from the values of several independent variables (IVs) X1,...,Xm in an optimal way. As a result of regression analysis we can use a regression function:

= A + B1X1+ …. + BmXm.

In this linear combination A is intercept, X1,...,Xm are independent variables and B1,...Bm are slopes for X1,...,Xm respectively [5].

CONCLUSION AND FUTURE WORK

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These model can further be enhance for real time application web interfaces .