SlideShare a Scribd company logo
1 of 4
Download to read offline
Pragya Jha et al Int. Journal of Engineering Research and Application
ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, pp.1280-1283

RESEARCH ARTICLE

www.ijera.com

OPEN ACCESS

Cost Estimation Using Parameterized For Use Case Point
Model (P-UCP)
Pragya Jha, Dr. Rajani Kanta Malu
(School of Computer science and Engineering KIIT University, Bhubaneswar, Odisha, India)
(Professor, School of Computer Engineering Kalinga Institute of Industrial Technology, KIIT University
Bhubaneswar, India)

ABSTRACT
Estimating the cost of development is one of the most important and scaring tasks for a software project
engineer. A lot of cost estimation models were reported become obsolete because of rapid changes on
technology. Earlier methods were only applicable in procedural software estimation. There is a paradigm shift
from procedural to object oriented software development. Gustav Karner [3] model for use case point estimation
method was further enhanced in this paper for better evaluation. Additionally use case narratives [4] and sixteen
most influential environment factors [2] were used for cost estimation.
Keywords- use case point method (UCP), Function Point, Software cost estimation.

I.

INTRODUCTION

Software, being an intangible product, is hard
to develop because there are so many unknowns in the
development process. Even when using a well-defined
methodology, the development cost of a well-defined
application is not easy to predict. Some key factors
that contribute to this difficulty include the precise set
of functionalities to be implemented, the risks
associated with the development process, and the
knowledge and experience of the development team.
Among these, the set of functionalities to be
implemented is the most influential factor. Function
Point (FP) metrics were designed to consider
functional requirements instead of lines of code.
Albrecht came up with the Function Point (FP)
metrics in 1979 [1]. FP uses five parameters: number
of inputs, number of outputs, number of inquiries,
number of internal logical files and number of external
logical files. Consequently, FP is based on the number
of interactions and the size of data to be used in the
end product. While FP eliminated the need for lines of
code or delivered source instructions, and was widely
used because of its independence on Development
platform and environment, it does not seem to be
applicable to software product developed using the
object-oriented methodology. In particular, the notion
of internal and external logical files is somewhat
harder to identify in the object-oriented paradigm.
Gustav Karner [3] came up with the notion of Use
Case Point (UCP) which is somewhat similar to the
notion of Function Point but based on use cases. One
of these methods or techniques is a Use case
modelling [6] that is a popular and widely used
technique for capturing and describing the functional
requirements of a software system. Periyasamy [4]
extend the UCP model with a focus on internal details
of each use case and Alwidian [2] that enhance sixteen
www.ijera.com

environment factor were used for the software
estimation. This paper describes the twenty four
environment factors which are strongly related to our
environment with use case narrative to show its effects
on cost estimation process.

II.

AIMS AND OBJECTIVES

The main objectives of this research is to
make UCP method applicable in environment by
adding the main of these factors that can be
summarized as follows [2]:
Satisfied Resources (SATR).
Financial Motives (FMOT).
Generalized Job Description (GJOB).
Client Type (CLIT).
Religious Events (REVT).
Unified Evaluation System (USYS).
Unscheduled Events (EVNT)
Continuous Training (TRAN).
Imposed Partner (IMPO).
Job Turnover (TURN).
Decision Making (DMAK).
Increasing Task (ITAX).
Unified IT Strategies (ITST).
Job Respect (RSPT).
Terminology Concept (TCON).
Income Level (INCM).
The extended UCP methodology uses every
aspect of a use case model such as actors, use cases,
associations between actors and use cases,
relationships between actors, relationships between
use cases and finally detailed narrative of each use
case. The last one is important because it describes the
missing details of a use case diagram, while others can
be directly extracted from a use case diagram itself.

1280 | P a g e
Pragya Jha et al Int. Journal of Engineering Research and Application
ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, pp.1280-1283
This makes the significant difference between UCP
method given by Karner [3] and e-UCP [4].
Table 1: Sample Use Case narrative
Use case name
A descriptive name of
the use case.
Purpose
A brief description of
the tasks to
be
implemented by this
use case
Input parameters
Input parameters to the
use case
Output parameters

Output
parameters
returned from this use
case.

Primary actor

The list of primary
actors who invoke this
use case.
The list of secondary
actors that are used by
this use case.

Secondary actor `

Precondition

Post-condition

Successful scenario

Exceptions

Includes list

Included by list
Extends list

Extended by list
Additional Remarks

Condition that must be
true before invoking
the
methods
that
implement this use
case.
Condition that must be
true after the methods
that implement this use
case complete.
A
sequence
of
instructions
that
explain the successful
scenario of invoking
this use case.
A set of conditions that
may make the use case
fail when invoked
Other use cases that
are included in this use
case.
Other use cases that
include this use case.
Other use cases that
are extended by this
use case.
Other use cases that
extend this use case.
Information
entered
here will be passed to
other UML models.

From the list of entries in Table 1, only the
following entries will be used by the e-UCP model:
Input parameters, Output parameters, Precondition,
Post-condition, Successful scenario, and Exceptions.
Other entries in the table are used for understanding
www.ijera.com

www.ijera.com

the application domain and for deriving other UML
diagrams [2].

III.

USE CASE POINT METHOD

Use case point method(hereinafter referred to
as UCP) is proposed by Gustav Karner [3] of
Objectory Company (renamed by rational company)
in 1993. This UCP contains steps:
3.1 Determine and compute the Unadjusted Use
Case Points (UUCP)
Unadjusted Use Case
Weight (UUCW) and Unadjusted Actor Weight
(UAW)
UUCP = UUCW+UAW
This step includes two inner steps as follow:Identify, classify and weight actors Each identified
actor is given a weighting from 1-3 as shown in
table1, that corresponds to simple, average, and
complex. Human actors are always classified as
complex and receive a weighting of 3. Systems to
which the new system will interface (legacy systems)
are either simple or average depending on the
mechanism by which they are addressed.
Table 2: Actors with weighting factors
Actor Type
Complexity
weighting
factor
Simple
1
Average
2
Complex
3
UAW=


 a Xw
i

i

Identify, classify and weight use cases

After calculating the Total actor weight, it is
necessary to create a use case model and to then
classify each use case as being simple, average or
complex. The method specifies ignoring use cases that
are associated through extend or include stereotypes.
This classification can be done depends on the basis of
the number of transactions in a use case.
Table 3: Use case with weighting factors
Use case
No of transaction Complexity
type
weighting factors
Simple
<=3
5
Average
4 to 7
10
Complex
>=7
15
m

UUCW=

 u Xw
i 1

i

i

3.2 Determine and compute the Technical
Complexity Factors (TCF)
Thirteen standard technical factors exist to
estimate the impact on productivity that various
1281 | P a g e
Pragya Jha et al Int. Journal of Engineering Research and Application
ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, pp.1280-1283
technical issues have on a project. Give each one of
these a rating on the scale of [0 – 5]. A ‘0’ rating
means the factor is irrelevant to the project, a ‘5’
rating means it is essential. Then for each factor it is
necessary to multiply its score by its weight and sum
the results. The final result is then substituted into a
Technical Complexity Factors .
Table 3: weighting factors for TCF
Environment Factors
Distributed systems
Application performance, objectives in
either response or throughput
End user efficiency
Complex internal processing
Reusability of source code
Installation ease
Ease of usability
Portability
Concurrency
Special security
Direct access for third parties
Special customer training provided
Ease to change

Weights
2
1
1
1
1
0.5
0.5
2
1
1
1
1
1

Technical Complexity Factor (TCF)
13

TCF=0.6+0.01

 fiXwi

www.ijera.com
8

ECF=1.4 + (-0.03)

 fiXwi
i 1

IV.

ADDITIONAL INFORMATION

Weights for Use case Narrative Parameters
A use case diagram must be supported by use
case narratives [4]. Table 1 shows the structure of a
use case narrative used in this methodology. Though
there is no standard for the structure of a use case
narrative, the authors found that the use case structure
illustrated in Table 1 contains all information that
many practitioners use. With this assumption, Table 5
describes the weights associated with the different
parameters of a use case narrative.
Table 5. Weights
Parameters

for

Use

Use case Narratives
Parameters
Input parameter
Output parameter
A predicate in
Precondition
A predicate in Post
condition
An action in successful
scenario
An exception

case

Narrative

Weights
0.1
0.1
0.1
0.1
0.2
0.1

i 1

m

3.3 Determine and compute the Environmental
Factors (ECF)
There is eight environmental factors. Each
factor is rated on a scale from [0-5]. Again, 0 means
the factor has a strong negative impact for the project.
3 is average. 5 mean it has a strong positive impact for
the project. Each factor is calculated by multiplying its
score by its weight and producing a sum of the results.
The final result becomes the Environmental
Complexity Factors.
Table 4: Weighting factors for ECF
Environment Factors
Weights

UUCN=

 uc
i 1

1.5
0.5
1
0.5
1
2
-1
2

Environmental Complexity Factor (ECF) =

www.ijera.com

Xwi

UCP method by using new environmental factors:
Adding a set of sixteen influencing and
suitable factors which strongly related to our
environment. These factors as shown in table 6. in
UCP method we have eight environmental factors and
by using the new factors, number of factors will be 24
factors, so that the equation of environmental
complexity factor will be :Environmental Complexity Factor (ECF)
24

=1.4 + (-0-03)

 F XW
i 1

Familiar with RUP
Application experience
Object oriented experience
Analyst Capability
Motivation
Stable Requirements
Part time workers
Difficult programming
language

n

i

i

Table 6: New Environment with weighting factor
Suggest Factors
Weight
Financial Motives
1.2
Generalized job description
0.9
Satisfied Resources
0.9
Client type
1.2
Religious Events
1.2
Continuous Training
1
Unified Evaluation system
0.8
Job Turnover
0.8
Imposed Partner
1.2
Decision Making
0.8
1282 | P a g e
Pragya Jha et al Int. Journal of Engineering Research and Application
ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, pp.1280-1283
Increasing Task
Unified IT Strategies
Job Respect
Terminology concept
Income Level

V.

1.2
0.9
0.7
0.8
1.2

OUR NEW METHOD TO
CALCULATE UUCP

UUCP=UAW+UUCW+UUCN
p-UCP (with additional factors TCF and UUCN)
p-UCP= UUCP*TCF*ECF

VI.

[4]

[5]

[6]

www.ijera.com

Software Engineering. PHI Learning Private
Limited. Yogesh Singh, Ruchika Malhotra
Kasi
Periyasamy and Aditi Ghode Department
of Computer Science University of
Wisconsin-La Crosse La Crosse, WI 54601
Cost Estimation using extended Use Case
Point (e-UCP) Model ,IEEE 2009.
Smith, J. The Estimation of Effort Based on
UseCases . Rational Software, White paper.
1999.
UML 2.0 Reference Manual, Object
Management Group, www.omg.org, 2003.

CONCLUSION

In this paper author exploring the use case
model in details that includes use case narrative and
sixteen new environment factor. As, the result, the
UCP calculated by new parameterized. The approach
was able to classify the software system with respect
to the level of code quality and the better way to
calculate the use case points.

VII.

FUTURE WORK

The paper is a framework for cost estimation
using the use case points, which can be finally
translated into lines of code [5]. The author of this
paper continue to work in UML class diagram which
will provide some sort of evolution of initial cost
estimation and also UCM relationship such as
<<include>> and <<extend>> should be investigated
in the cost estimation process. The authors have also
planned to revise these factors in order to improve
the calculations. Development of concurrent and real
time systems is a challenging job now a day that needs
to be estimated.

VIII.

ACKNOWLEDGEMENTS

This work was supported by KIIT University,
Bhubaneswar, India. Our thanks to respected G. N.
JHA for his valuable suggestions during various
discussion sessions .

REFERENCES
[1]

[2]

[3]

Albrecht, A.J., 1979. Measuring Application
Development
productivity.
IBM
Applications Development Symp, GUIDE
Int. and SHARE
Inc., IBM Corp.,
Monterey, CA, Oct. 14-17, 1979.
Jaber
Alwidian,
Wael
Hadi
ITC
department CIS department Arab Open
University Philadelphia University RiyadhSaudi Arabia Amman- Jordan Enhancing the
Results of UCP in Cost Estimation Using
New External Environmental Factors ,IEEE
2012, 2012 International Conference on
Information Technology and e-Services
Karner's works on use case points was
written in his diploma thesis titled "Metrics
Objectory", Page no-126. Object Oriented

www.ijera.com

1283 | P a g e

More Related Content

What's hot

Karner resource estimation for objectory projects
Karner   resource estimation for objectory projectsKarner   resource estimation for objectory projects
Karner resource estimation for objectory projectsOcho08
 
IRJET- Comparison of Classification Algorithms using Machine Learning
IRJET- Comparison of Classification Algorithms using Machine LearningIRJET- Comparison of Classification Algorithms using Machine Learning
IRJET- Comparison of Classification Algorithms using Machine LearningIRJET Journal
 
Sca a sine cosine algorithm for solving optimization problems
Sca a sine cosine algorithm for solving optimization problemsSca a sine cosine algorithm for solving optimization problems
Sca a sine cosine algorithm for solving optimization problemslaxmanLaxman03209
 
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...ijcsa
 
A Hierarchical Feature Set optimization for effective code change based Defec...
A Hierarchical Feature Set optimization for effective code change based Defec...A Hierarchical Feature Set optimization for effective code change based Defec...
A Hierarchical Feature Set optimization for effective code change based Defec...IOSR Journals
 
Rachit Mishra_stock prediction_report
Rachit Mishra_stock prediction_reportRachit Mishra_stock prediction_report
Rachit Mishra_stock prediction_reportRachit Mishra
 
A GUI-driven prototype for synthesizing self-adaptation decision
A GUI-driven prototype for synthesizing self-adaptation decisionA GUI-driven prototype for synthesizing self-adaptation decision
A GUI-driven prototype for synthesizing self-adaptation decisionjournalBEEI
 
Regression test selection model: a comparison between ReTSE and pythia
Regression test selection model: a comparison between ReTSE and pythiaRegression test selection model: a comparison between ReTSE and pythia
Regression test selection model: a comparison between ReTSE and pythiaTELKOMNIKA JOURNAL
 
Using Data Mining to Identify COSMIC Function Point Measurement Competence
Using Data Mining to Identify COSMIC Function Point Measurement Competence  Using Data Mining to Identify COSMIC Function Point Measurement Competence
Using Data Mining to Identify COSMIC Function Point Measurement Competence IJECEIAES
 
IRJET- Machine Learning Techniques for Code Optimization
IRJET-  	  Machine Learning Techniques for Code OptimizationIRJET-  	  Machine Learning Techniques for Code Optimization
IRJET- Machine Learning Techniques for Code OptimizationIRJET Journal
 
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATA
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATABINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATA
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATAacijjournal
 
Harnessing deep learning algorithms to predict software refactoring
Harnessing deep learning algorithms to predict software refactoringHarnessing deep learning algorithms to predict software refactoring
Harnessing deep learning algorithms to predict software refactoringTELKOMNIKA JOURNAL
 
Analyzing the solutions of DEA through information visualization and data min...
Analyzing the solutions of DEA through information visualization and data min...Analyzing the solutions of DEA through information visualization and data min...
Analyzing the solutions of DEA through information visualization and data min...ertekg
 
Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...
Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...
Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...Kashif Mehmood
 
IRJET - Movie Genre Prediction from Plot Summaries by Comparing Various C...
IRJET -  	  Movie Genre Prediction from Plot Summaries by Comparing Various C...IRJET -  	  Movie Genre Prediction from Plot Summaries by Comparing Various C...
IRJET - Movie Genre Prediction from Plot Summaries by Comparing Various C...IRJET Journal
 

What's hot (18)

Karner resource estimation for objectory projects
Karner   resource estimation for objectory projectsKarner   resource estimation for objectory projects
Karner resource estimation for objectory projects
 
IRJET- Comparison of Classification Algorithms using Machine Learning
IRJET- Comparison of Classification Algorithms using Machine LearningIRJET- Comparison of Classification Algorithms using Machine Learning
IRJET- Comparison of Classification Algorithms using Machine Learning
 
Sca a sine cosine algorithm for solving optimization problems
Sca a sine cosine algorithm for solving optimization problemsSca a sine cosine algorithm for solving optimization problems
Sca a sine cosine algorithm for solving optimization problems
 
Ijetcas14 468
Ijetcas14 468Ijetcas14 468
Ijetcas14 468
 
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...
 
A Hierarchical Feature Set optimization for effective code change based Defec...
A Hierarchical Feature Set optimization for effective code change based Defec...A Hierarchical Feature Set optimization for effective code change based Defec...
A Hierarchical Feature Set optimization for effective code change based Defec...
 
50120130406033
5012013040603350120130406033
50120130406033
 
Rachit Mishra_stock prediction_report
Rachit Mishra_stock prediction_reportRachit Mishra_stock prediction_report
Rachit Mishra_stock prediction_report
 
Q01231103109
Q01231103109Q01231103109
Q01231103109
 
A GUI-driven prototype for synthesizing self-adaptation decision
A GUI-driven prototype for synthesizing self-adaptation decisionA GUI-driven prototype for synthesizing self-adaptation decision
A GUI-driven prototype for synthesizing self-adaptation decision
 
Regression test selection model: a comparison between ReTSE and pythia
Regression test selection model: a comparison between ReTSE and pythiaRegression test selection model: a comparison between ReTSE and pythia
Regression test selection model: a comparison between ReTSE and pythia
 
Using Data Mining to Identify COSMIC Function Point Measurement Competence
Using Data Mining to Identify COSMIC Function Point Measurement Competence  Using Data Mining to Identify COSMIC Function Point Measurement Competence
Using Data Mining to Identify COSMIC Function Point Measurement Competence
 
IRJET- Machine Learning Techniques for Code Optimization
IRJET-  	  Machine Learning Techniques for Code OptimizationIRJET-  	  Machine Learning Techniques for Code Optimization
IRJET- Machine Learning Techniques for Code Optimization
 
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATA
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATABINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATA
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATA
 
Harnessing deep learning algorithms to predict software refactoring
Harnessing deep learning algorithms to predict software refactoringHarnessing deep learning algorithms to predict software refactoring
Harnessing deep learning algorithms to predict software refactoring
 
Analyzing the solutions of DEA through information visualization and data min...
Analyzing the solutions of DEA through information visualization and data min...Analyzing the solutions of DEA through information visualization and data min...
Analyzing the solutions of DEA through information visualization and data min...
 
Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...
Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...
Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...
 
IRJET - Movie Genre Prediction from Plot Summaries by Comparing Various C...
IRJET -  	  Movie Genre Prediction from Plot Summaries by Comparing Various C...IRJET -  	  Movie Genre Prediction from Plot Summaries by Comparing Various C...
IRJET - Movie Genre Prediction from Plot Summaries by Comparing Various C...
 

Viewers also liked (20)

Ga3510571061
Ga3510571061Ga3510571061
Ga3510571061
 
Gd3510871090
Gd3510871090Gd3510871090
Gd3510871090
 
Fv3510271037
Fv3510271037Fv3510271037
Fv3510271037
 
Gv3512031207
Gv3512031207Gv3512031207
Gv3512031207
 
Gw3512081212
Gw3512081212Gw3512081212
Gw3512081212
 
Gl3511331144
Gl3511331144Gl3511331144
Gl3511331144
 
Gq3511781181
Gq3511781181Gq3511781181
Gq3511781181
 
Hg3512751279
Hg3512751279Hg3512751279
Hg3512751279
 
Ha3512291233
Ha3512291233Ha3512291233
Ha3512291233
 
Gk3511271132
Gk3511271132Gk3511271132
Gk3511271132
 
Gh3511131117
Gh3511131117Gh3511131117
Gh3511131117
 
Gg3511071112
Gg3511071112Gg3511071112
Gg3511071112
 
Gc3510651086
Gc3510651086Gc3510651086
Gc3510651086
 
Gj3511231126
Gj3511231126Gj3511231126
Gj3511231126
 
Fx3510401044
Fx3510401044Fx3510401044
Fx3510401044
 
Gt3511931198
Gt3511931198Gt3511931198
Gt3511931198
 
Ge3510911102
Ge3510911102Ge3510911102
Ge3510911102
 
Gz3512221228
Gz3512221228Gz3512221228
Gz3512221228
 
Hf3512571274
Hf3512571274Hf3512571274
Hf3512571274
 
Gy3512171221
Gy3512171221Gy3512171221
Gy3512171221
 

Similar to Hh3512801283

Test Case Optimization and Redundancy Reduction Using GA and Neural Networks
Test Case Optimization and Redundancy Reduction Using GA and Neural Networks Test Case Optimization and Redundancy Reduction Using GA and Neural Networks
Test Case Optimization and Redundancy Reduction Using GA and Neural Networks IJECEIAES
 
Size and Time Estimation in Goal Graph Using Use Case Points (UCP): A Survey
Size and Time Estimation in Goal Graph Using Use Case Points (UCP): A SurveySize and Time Estimation in Goal Graph Using Use Case Points (UCP): A Survey
Size and Time Estimation in Goal Graph Using Use Case Points (UCP): A SurveyIJERA Editor
 
Software testing effort estimation with cobb douglas function a practical app...
Software testing effort estimation with cobb douglas function a practical app...Software testing effort estimation with cobb douglas function a practical app...
Software testing effort estimation with cobb douglas function a practical app...eSAT Publishing House
 
Software testing effort estimation with cobb douglas function- a practical ap...
Software testing effort estimation with cobb douglas function- a practical ap...Software testing effort estimation with cobb douglas function- a practical ap...
Software testing effort estimation with cobb douglas function- a practical ap...eSAT Journals
 
Artificial Intelligence based Pattern Recognition
Artificial Intelligence based Pattern RecognitionArtificial Intelligence based Pattern Recognition
Artificial Intelligence based Pattern RecognitionDr. Amarjeet Singh
 
Configuration Navigation Analysis Model for Regression Test Case Prioritization
Configuration Navigation Analysis Model for Regression Test Case PrioritizationConfiguration Navigation Analysis Model for Regression Test Case Prioritization
Configuration Navigation Analysis Model for Regression Test Case Prioritizationijsrd.com
 
SIZE ESTIMATION OF OLAP SYSTEMS
SIZE ESTIMATION OF OLAP SYSTEMSSIZE ESTIMATION OF OLAP SYSTEMS
SIZE ESTIMATION OF OLAP SYSTEMScscpconf
 
Size estimation of olap systems
Size estimation of olap systemsSize estimation of olap systems
Size estimation of olap systemscsandit
 
Handwritten Text Recognition Using Machine Learning
Handwritten Text Recognition Using Machine LearningHandwritten Text Recognition Using Machine Learning
Handwritten Text Recognition Using Machine LearningIRJET Journal
 
Testing-as-a-Service (TaaS) – Capabilities and Features for Real-Time Testing...
Testing-as-a-Service (TaaS) – Capabilities and Features for Real-Time Testing...Testing-as-a-Service (TaaS) – Capabilities and Features for Real-Time Testing...
Testing-as-a-Service (TaaS) – Capabilities and Features for Real-Time Testing...AIRCC Publishing Corporation
 
TESTING-AS-A-SERVICE (TAAS) – CAPABILITIES AND FEATURES FOR REAL-TIME TESTING...
TESTING-AS-A-SERVICE (TAAS) – CAPABILITIES AND FEATURES FOR REAL-TIME TESTING...TESTING-AS-A-SERVICE (TAAS) – CAPABILITIES AND FEATURES FOR REAL-TIME TESTING...
TESTING-AS-A-SERVICE (TAAS) – CAPABILITIES AND FEATURES FOR REAL-TIME TESTING...ijcsit
 
Benchmarking Techniques for Performance Analysis of Operating Systems and Pro...
Benchmarking Techniques for Performance Analysis of Operating Systems and Pro...Benchmarking Techniques for Performance Analysis of Operating Systems and Pro...
Benchmarking Techniques for Performance Analysis of Operating Systems and Pro...IRJET Journal
 
IRJET - A Novel Approach for Software Defect Prediction based on Dimensio...
IRJET -  	  A Novel Approach for Software Defect Prediction based on Dimensio...IRJET -  	  A Novel Approach for Software Defect Prediction based on Dimensio...
IRJET - A Novel Approach for Software Defect Prediction based on Dimensio...IRJET Journal
 
Comparative Analysis of Machine Learning Algorithms for their Effectiveness i...
Comparative Analysis of Machine Learning Algorithms for their Effectiveness i...Comparative Analysis of Machine Learning Algorithms for their Effectiveness i...
Comparative Analysis of Machine Learning Algorithms for their Effectiveness i...IRJET Journal
 
Review on Algorithmic and Non Algorithmic Software Cost Estimation Techniques
Review on Algorithmic and Non Algorithmic Software Cost Estimation TechniquesReview on Algorithmic and Non Algorithmic Software Cost Estimation Techniques
Review on Algorithmic and Non Algorithmic Software Cost Estimation Techniquesijtsrd
 
Conceptualization of a Domain Specific Simulator for Requirements Prioritization
Conceptualization of a Domain Specific Simulator for Requirements PrioritizationConceptualization of a Domain Specific Simulator for Requirements Prioritization
Conceptualization of a Domain Specific Simulator for Requirements Prioritizationresearchinventy
 
Software estimation techniques
Software estimation techniquesSoftware estimation techniques
Software estimation techniquesTan Tran
 
Model based test case prioritization using neural network classification
Model based test case prioritization using neural network classificationModel based test case prioritization using neural network classification
Model based test case prioritization using neural network classificationcseij
 

Similar to Hh3512801283 (20)

Test Case Optimization and Redundancy Reduction Using GA and Neural Networks
Test Case Optimization and Redundancy Reduction Using GA and Neural Networks Test Case Optimization and Redundancy Reduction Using GA and Neural Networks
Test Case Optimization and Redundancy Reduction Using GA and Neural Networks
 
Size and Time Estimation in Goal Graph Using Use Case Points (UCP): A Survey
Size and Time Estimation in Goal Graph Using Use Case Points (UCP): A SurveySize and Time Estimation in Goal Graph Using Use Case Points (UCP): A Survey
Size and Time Estimation in Goal Graph Using Use Case Points (UCP): A Survey
 
Software testing effort estimation with cobb douglas function a practical app...
Software testing effort estimation with cobb douglas function a practical app...Software testing effort estimation with cobb douglas function a practical app...
Software testing effort estimation with cobb douglas function a practical app...
 
Software testing effort estimation with cobb douglas function- a practical ap...
Software testing effort estimation with cobb douglas function- a practical ap...Software testing effort estimation with cobb douglas function- a practical ap...
Software testing effort estimation with cobb douglas function- a practical ap...
 
Artificial Intelligence based Pattern Recognition
Artificial Intelligence based Pattern RecognitionArtificial Intelligence based Pattern Recognition
Artificial Intelligence based Pattern Recognition
 
Configuration Navigation Analysis Model for Regression Test Case Prioritization
Configuration Navigation Analysis Model for Regression Test Case PrioritizationConfiguration Navigation Analysis Model for Regression Test Case Prioritization
Configuration Navigation Analysis Model for Regression Test Case Prioritization
 
SIZE ESTIMATION OF OLAP SYSTEMS
SIZE ESTIMATION OF OLAP SYSTEMSSIZE ESTIMATION OF OLAP SYSTEMS
SIZE ESTIMATION OF OLAP SYSTEMS
 
Size estimation of olap systems
Size estimation of olap systemsSize estimation of olap systems
Size estimation of olap systems
 
Handwritten Text Recognition Using Machine Learning
Handwritten Text Recognition Using Machine LearningHandwritten Text Recognition Using Machine Learning
Handwritten Text Recognition Using Machine Learning
 
Costing ass4
Costing ass4Costing ass4
Costing ass4
 
Ie3514301434
Ie3514301434Ie3514301434
Ie3514301434
 
Testing-as-a-Service (TaaS) – Capabilities and Features for Real-Time Testing...
Testing-as-a-Service (TaaS) – Capabilities and Features for Real-Time Testing...Testing-as-a-Service (TaaS) – Capabilities and Features for Real-Time Testing...
Testing-as-a-Service (TaaS) – Capabilities and Features for Real-Time Testing...
 
TESTING-AS-A-SERVICE (TAAS) – CAPABILITIES AND FEATURES FOR REAL-TIME TESTING...
TESTING-AS-A-SERVICE (TAAS) – CAPABILITIES AND FEATURES FOR REAL-TIME TESTING...TESTING-AS-A-SERVICE (TAAS) – CAPABILITIES AND FEATURES FOR REAL-TIME TESTING...
TESTING-AS-A-SERVICE (TAAS) – CAPABILITIES AND FEATURES FOR REAL-TIME TESTING...
 
Benchmarking Techniques for Performance Analysis of Operating Systems and Pro...
Benchmarking Techniques for Performance Analysis of Operating Systems and Pro...Benchmarking Techniques for Performance Analysis of Operating Systems and Pro...
Benchmarking Techniques for Performance Analysis of Operating Systems and Pro...
 
IRJET - A Novel Approach for Software Defect Prediction based on Dimensio...
IRJET -  	  A Novel Approach for Software Defect Prediction based on Dimensio...IRJET -  	  A Novel Approach for Software Defect Prediction based on Dimensio...
IRJET - A Novel Approach for Software Defect Prediction based on Dimensio...
 
Comparative Analysis of Machine Learning Algorithms for their Effectiveness i...
Comparative Analysis of Machine Learning Algorithms for their Effectiveness i...Comparative Analysis of Machine Learning Algorithms for their Effectiveness i...
Comparative Analysis of Machine Learning Algorithms for their Effectiveness i...
 
Review on Algorithmic and Non Algorithmic Software Cost Estimation Techniques
Review on Algorithmic and Non Algorithmic Software Cost Estimation TechniquesReview on Algorithmic and Non Algorithmic Software Cost Estimation Techniques
Review on Algorithmic and Non Algorithmic Software Cost Estimation Techniques
 
Conceptualization of a Domain Specific Simulator for Requirements Prioritization
Conceptualization of a Domain Specific Simulator for Requirements PrioritizationConceptualization of a Domain Specific Simulator for Requirements Prioritization
Conceptualization of a Domain Specific Simulator for Requirements Prioritization
 
Software estimation techniques
Software estimation techniquesSoftware estimation techniques
Software estimation techniques
 
Model based test case prioritization using neural network classification
Model based test case prioritization using neural network classificationModel based test case prioritization using neural network classification
Model based test case prioritization using neural network classification
 

Recently uploaded

Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxRustici Software
 
The Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightThe Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightSafe Software
 
AI in Action: Real World Use Cases by Anitaraj
AI in Action: Real World Use Cases by AnitarajAI in Action: Real World Use Cases by Anitaraj
AI in Action: Real World Use Cases by AnitarajAnitaRaj43
 
Less Is More: Utilizing Ballerina to Architect a Cloud Data Platform
Less Is More: Utilizing Ballerina to Architect a Cloud Data PlatformLess Is More: Utilizing Ballerina to Architect a Cloud Data Platform
Less Is More: Utilizing Ballerina to Architect a Cloud Data PlatformWSO2
 
JohnPollard-hybrid-app-RailsConf2024.pptx
JohnPollard-hybrid-app-RailsConf2024.pptxJohnPollard-hybrid-app-RailsConf2024.pptx
JohnPollard-hybrid-app-RailsConf2024.pptxJohnPollard37
 
Navigating Identity and Access Management in the Modern Enterprise
Navigating Identity and Access Management in the Modern EnterpriseNavigating Identity and Access Management in the Modern Enterprise
Navigating Identity and Access Management in the Modern EnterpriseWSO2
 
Quantum Leap in Next-Generation Computing
Quantum Leap in Next-Generation ComputingQuantum Leap in Next-Generation Computing
Quantum Leap in Next-Generation ComputingWSO2
 
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...TrustArc
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdfSandro Moreira
 
Modernizing Legacy Systems Using Ballerina
Modernizing Legacy Systems Using BallerinaModernizing Legacy Systems Using Ballerina
Modernizing Legacy Systems Using BallerinaWSO2
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodJuan lago vázquez
 
ChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps ProductivityChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps ProductivityVictorSzoltysek
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Zilliz
 
JavaScript Usage Statistics 2024 - The Ultimate Guide
JavaScript Usage Statistics 2024 - The Ultimate GuideJavaScript Usage Statistics 2024 - The Ultimate Guide
JavaScript Usage Statistics 2024 - The Ultimate GuidePixlogix Infotech
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Victor Rentea
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...DianaGray10
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAndrey Devyatkin
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistandanishmna97
 
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityWSO2
 

Recently uploaded (20)

Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
The Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightThe Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and Insight
 
AI in Action: Real World Use Cases by Anitaraj
AI in Action: Real World Use Cases by AnitarajAI in Action: Real World Use Cases by Anitaraj
AI in Action: Real World Use Cases by Anitaraj
 
Less Is More: Utilizing Ballerina to Architect a Cloud Data Platform
Less Is More: Utilizing Ballerina to Architect a Cloud Data PlatformLess Is More: Utilizing Ballerina to Architect a Cloud Data Platform
Less Is More: Utilizing Ballerina to Architect a Cloud Data Platform
 
JohnPollard-hybrid-app-RailsConf2024.pptx
JohnPollard-hybrid-app-RailsConf2024.pptxJohnPollard-hybrid-app-RailsConf2024.pptx
JohnPollard-hybrid-app-RailsConf2024.pptx
 
Navigating Identity and Access Management in the Modern Enterprise
Navigating Identity and Access Management in the Modern EnterpriseNavigating Identity and Access Management in the Modern Enterprise
Navigating Identity and Access Management in the Modern Enterprise
 
Quantum Leap in Next-Generation Computing
Quantum Leap in Next-Generation ComputingQuantum Leap in Next-Generation Computing
Quantum Leap in Next-Generation Computing
 
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
Modernizing Legacy Systems Using Ballerina
Modernizing Legacy Systems Using BallerinaModernizing Legacy Systems Using Ballerina
Modernizing Legacy Systems Using Ballerina
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
ChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps ProductivityChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps Productivity
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
JavaScript Usage Statistics 2024 - The Ultimate Guide
JavaScript Usage Statistics 2024 - The Ultimate GuideJavaScript Usage Statistics 2024 - The Ultimate Guide
JavaScript Usage Statistics 2024 - The Ultimate Guide
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital Adaptability
 

Hh3512801283

  • 1. Pragya Jha et al Int. Journal of Engineering Research and Application ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, pp.1280-1283 RESEARCH ARTICLE www.ijera.com OPEN ACCESS Cost Estimation Using Parameterized For Use Case Point Model (P-UCP) Pragya Jha, Dr. Rajani Kanta Malu (School of Computer science and Engineering KIIT University, Bhubaneswar, Odisha, India) (Professor, School of Computer Engineering Kalinga Institute of Industrial Technology, KIIT University Bhubaneswar, India) ABSTRACT Estimating the cost of development is one of the most important and scaring tasks for a software project engineer. A lot of cost estimation models were reported become obsolete because of rapid changes on technology. Earlier methods were only applicable in procedural software estimation. There is a paradigm shift from procedural to object oriented software development. Gustav Karner [3] model for use case point estimation method was further enhanced in this paper for better evaluation. Additionally use case narratives [4] and sixteen most influential environment factors [2] were used for cost estimation. Keywords- use case point method (UCP), Function Point, Software cost estimation. I. INTRODUCTION Software, being an intangible product, is hard to develop because there are so many unknowns in the development process. Even when using a well-defined methodology, the development cost of a well-defined application is not easy to predict. Some key factors that contribute to this difficulty include the precise set of functionalities to be implemented, the risks associated with the development process, and the knowledge and experience of the development team. Among these, the set of functionalities to be implemented is the most influential factor. Function Point (FP) metrics were designed to consider functional requirements instead of lines of code. Albrecht came up with the Function Point (FP) metrics in 1979 [1]. FP uses five parameters: number of inputs, number of outputs, number of inquiries, number of internal logical files and number of external logical files. Consequently, FP is based on the number of interactions and the size of data to be used in the end product. While FP eliminated the need for lines of code or delivered source instructions, and was widely used because of its independence on Development platform and environment, it does not seem to be applicable to software product developed using the object-oriented methodology. In particular, the notion of internal and external logical files is somewhat harder to identify in the object-oriented paradigm. Gustav Karner [3] came up with the notion of Use Case Point (UCP) which is somewhat similar to the notion of Function Point but based on use cases. One of these methods or techniques is a Use case modelling [6] that is a popular and widely used technique for capturing and describing the functional requirements of a software system. Periyasamy [4] extend the UCP model with a focus on internal details of each use case and Alwidian [2] that enhance sixteen www.ijera.com environment factor were used for the software estimation. This paper describes the twenty four environment factors which are strongly related to our environment with use case narrative to show its effects on cost estimation process. II. AIMS AND OBJECTIVES The main objectives of this research is to make UCP method applicable in environment by adding the main of these factors that can be summarized as follows [2]: Satisfied Resources (SATR). Financial Motives (FMOT). Generalized Job Description (GJOB). Client Type (CLIT). Religious Events (REVT). Unified Evaluation System (USYS). Unscheduled Events (EVNT) Continuous Training (TRAN). Imposed Partner (IMPO). Job Turnover (TURN). Decision Making (DMAK). Increasing Task (ITAX). Unified IT Strategies (ITST). Job Respect (RSPT). Terminology Concept (TCON). Income Level (INCM). The extended UCP methodology uses every aspect of a use case model such as actors, use cases, associations between actors and use cases, relationships between actors, relationships between use cases and finally detailed narrative of each use case. The last one is important because it describes the missing details of a use case diagram, while others can be directly extracted from a use case diagram itself. 1280 | P a g e
  • 2. Pragya Jha et al Int. Journal of Engineering Research and Application ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, pp.1280-1283 This makes the significant difference between UCP method given by Karner [3] and e-UCP [4]. Table 1: Sample Use Case narrative Use case name A descriptive name of the use case. Purpose A brief description of the tasks to be implemented by this use case Input parameters Input parameters to the use case Output parameters Output parameters returned from this use case. Primary actor The list of primary actors who invoke this use case. The list of secondary actors that are used by this use case. Secondary actor ` Precondition Post-condition Successful scenario Exceptions Includes list Included by list Extends list Extended by list Additional Remarks Condition that must be true before invoking the methods that implement this use case. Condition that must be true after the methods that implement this use case complete. A sequence of instructions that explain the successful scenario of invoking this use case. A set of conditions that may make the use case fail when invoked Other use cases that are included in this use case. Other use cases that include this use case. Other use cases that are extended by this use case. Other use cases that extend this use case. Information entered here will be passed to other UML models. From the list of entries in Table 1, only the following entries will be used by the e-UCP model: Input parameters, Output parameters, Precondition, Post-condition, Successful scenario, and Exceptions. Other entries in the table are used for understanding www.ijera.com www.ijera.com the application domain and for deriving other UML diagrams [2]. III. USE CASE POINT METHOD Use case point method(hereinafter referred to as UCP) is proposed by Gustav Karner [3] of Objectory Company (renamed by rational company) in 1993. This UCP contains steps: 3.1 Determine and compute the Unadjusted Use Case Points (UUCP) Unadjusted Use Case Weight (UUCW) and Unadjusted Actor Weight (UAW) UUCP = UUCW+UAW This step includes two inner steps as follow:Identify, classify and weight actors Each identified actor is given a weighting from 1-3 as shown in table1, that corresponds to simple, average, and complex. Human actors are always classified as complex and receive a weighting of 3. Systems to which the new system will interface (legacy systems) are either simple or average depending on the mechanism by which they are addressed. Table 2: Actors with weighting factors Actor Type Complexity weighting factor Simple 1 Average 2 Complex 3 UAW=   a Xw i i Identify, classify and weight use cases After calculating the Total actor weight, it is necessary to create a use case model and to then classify each use case as being simple, average or complex. The method specifies ignoring use cases that are associated through extend or include stereotypes. This classification can be done depends on the basis of the number of transactions in a use case. Table 3: Use case with weighting factors Use case No of transaction Complexity type weighting factors Simple <=3 5 Average 4 to 7 10 Complex >=7 15 m UUCW=  u Xw i 1 i i 3.2 Determine and compute the Technical Complexity Factors (TCF) Thirteen standard technical factors exist to estimate the impact on productivity that various 1281 | P a g e
  • 3. Pragya Jha et al Int. Journal of Engineering Research and Application ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, pp.1280-1283 technical issues have on a project. Give each one of these a rating on the scale of [0 – 5]. A ‘0’ rating means the factor is irrelevant to the project, a ‘5’ rating means it is essential. Then for each factor it is necessary to multiply its score by its weight and sum the results. The final result is then substituted into a Technical Complexity Factors . Table 3: weighting factors for TCF Environment Factors Distributed systems Application performance, objectives in either response or throughput End user efficiency Complex internal processing Reusability of source code Installation ease Ease of usability Portability Concurrency Special security Direct access for third parties Special customer training provided Ease to change Weights 2 1 1 1 1 0.5 0.5 2 1 1 1 1 1 Technical Complexity Factor (TCF) 13 TCF=0.6+0.01  fiXwi www.ijera.com 8 ECF=1.4 + (-0.03)  fiXwi i 1 IV. ADDITIONAL INFORMATION Weights for Use case Narrative Parameters A use case diagram must be supported by use case narratives [4]. Table 1 shows the structure of a use case narrative used in this methodology. Though there is no standard for the structure of a use case narrative, the authors found that the use case structure illustrated in Table 1 contains all information that many practitioners use. With this assumption, Table 5 describes the weights associated with the different parameters of a use case narrative. Table 5. Weights Parameters for Use Use case Narratives Parameters Input parameter Output parameter A predicate in Precondition A predicate in Post condition An action in successful scenario An exception case Narrative Weights 0.1 0.1 0.1 0.1 0.2 0.1 i 1 m 3.3 Determine and compute the Environmental Factors (ECF) There is eight environmental factors. Each factor is rated on a scale from [0-5]. Again, 0 means the factor has a strong negative impact for the project. 3 is average. 5 mean it has a strong positive impact for the project. Each factor is calculated by multiplying its score by its weight and producing a sum of the results. The final result becomes the Environmental Complexity Factors. Table 4: Weighting factors for ECF Environment Factors Weights UUCN=  uc i 1 1.5 0.5 1 0.5 1 2 -1 2 Environmental Complexity Factor (ECF) = www.ijera.com Xwi UCP method by using new environmental factors: Adding a set of sixteen influencing and suitable factors which strongly related to our environment. These factors as shown in table 6. in UCP method we have eight environmental factors and by using the new factors, number of factors will be 24 factors, so that the equation of environmental complexity factor will be :Environmental Complexity Factor (ECF) 24 =1.4 + (-0-03)  F XW i 1 Familiar with RUP Application experience Object oriented experience Analyst Capability Motivation Stable Requirements Part time workers Difficult programming language n i i Table 6: New Environment with weighting factor Suggest Factors Weight Financial Motives 1.2 Generalized job description 0.9 Satisfied Resources 0.9 Client type 1.2 Religious Events 1.2 Continuous Training 1 Unified Evaluation system 0.8 Job Turnover 0.8 Imposed Partner 1.2 Decision Making 0.8 1282 | P a g e
  • 4. Pragya Jha et al Int. Journal of Engineering Research and Application ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, pp.1280-1283 Increasing Task Unified IT Strategies Job Respect Terminology concept Income Level V. 1.2 0.9 0.7 0.8 1.2 OUR NEW METHOD TO CALCULATE UUCP UUCP=UAW+UUCW+UUCN p-UCP (with additional factors TCF and UUCN) p-UCP= UUCP*TCF*ECF VI. [4] [5] [6] www.ijera.com Software Engineering. PHI Learning Private Limited. Yogesh Singh, Ruchika Malhotra Kasi Periyasamy and Aditi Ghode Department of Computer Science University of Wisconsin-La Crosse La Crosse, WI 54601 Cost Estimation using extended Use Case Point (e-UCP) Model ,IEEE 2009. Smith, J. The Estimation of Effort Based on UseCases . Rational Software, White paper. 1999. UML 2.0 Reference Manual, Object Management Group, www.omg.org, 2003. CONCLUSION In this paper author exploring the use case model in details that includes use case narrative and sixteen new environment factor. As, the result, the UCP calculated by new parameterized. The approach was able to classify the software system with respect to the level of code quality and the better way to calculate the use case points. VII. FUTURE WORK The paper is a framework for cost estimation using the use case points, which can be finally translated into lines of code [5]. The author of this paper continue to work in UML class diagram which will provide some sort of evolution of initial cost estimation and also UCM relationship such as <<include>> and <<extend>> should be investigated in the cost estimation process. The authors have also planned to revise these factors in order to improve the calculations. Development of concurrent and real time systems is a challenging job now a day that needs to be estimated. VIII. ACKNOWLEDGEMENTS This work was supported by KIIT University, Bhubaneswar, India. Our thanks to respected G. N. JHA for his valuable suggestions during various discussion sessions . REFERENCES [1] [2] [3] Albrecht, A.J., 1979. Measuring Application Development productivity. IBM Applications Development Symp, GUIDE Int. and SHARE Inc., IBM Corp., Monterey, CA, Oct. 14-17, 1979. Jaber Alwidian, Wael Hadi ITC department CIS department Arab Open University Philadelphia University RiyadhSaudi Arabia Amman- Jordan Enhancing the Results of UCP in Cost Estimation Using New External Environmental Factors ,IEEE 2012, 2012 International Conference on Information Technology and e-Services Karner's works on use case points was written in his diploma thesis titled "Metrics Objectory", Page no-126. Object Oriented www.ijera.com 1283 | P a g e