International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue6- June 2013
Intelligent Models Creation for cost estimation using
Clustering techniques
Prof. N. Suresh HOD/CSE
Kurinji College of Engineering and
Technology , Manapparai – 621 307.
Dr. D. Manimegalai M. E., Ph. D.,
Prof & Head / IT
National Engineering College, Kovilpatti.
Abstract:
Software has played an increasingly important role in systems acquisition, engineering and
development particularly for large complex systems. Software development may include research, new
development, modification, reuse, re-engineering, maintenance or any other activities that result in
software products. The software development methodology is a framework that is used to structure; plan
and control the process of developing information systems. A wide variety of such frameworks have
evolved over the years, each with its own recognized strengths and weakness. System development
methodology is not necessarily suitable to be used by all projects, based on various technical,
organizational, project and team considerations. Software development effort estimation is the process of
predicting the most realistic use of effort required to develop or maintain software based on incomplete,
uncertain and noisy input. Effort estimated may be used as input to project plans, iteration plans, budgets,
and investment analyses, pricing processes and bidding rounds the term many ways of categorizing
estimation, approaches. The top level categories are expert estimation, formal estimation, combinationbased estimation, statistical estimation. The ability to accurately estimate the time taken and cost for a
project to come in to its successful conclusion is serious problem for software engineers. Accurate
estimation of the software costs is a critical part of effective program management. The practice of
predicting the cost of software has evolved, but it is far from perfect. The perfect cost-estimation method
recommends an approach to improve the utility of the software cost estimated by exposing uncertainty (in
understanding of the project as well as in costing accuracy) and reducing the risk that the estimate will be
far different from the actual cost. My proposed work is to frame a cost estimation model in the approach
to improve the utility of the software cost estimated by exposing uncertainty.
I. Introduction:
Software cost estimation is the difficult
task and important task for successful projects.
Using software cost estimation technique predict
the time and cost to build successful software
development. These kinds of operations are
performed by client or developer in
implementation. These operations are performs
based on different parameters. Those parameters
are project size and lines of code.
Many
software
cost
estimation
techniques are introduced for development of
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high quality project. Existing software cost
estimation techniques are predicts the less
number of parameters. Those parameters are not
gives the proper implementation steps
environment. Previous Agile, COCOMO,
COCOMO II are predicts the different kinds
estimation techniques based on variables.
Algorithm based estimation techniques are not
gives the proper solution in implementation.
After some number of days Non
Algorithmic cost estimation is gives the proper
solution in implementation specification. Now
we are introduces in this paper generational
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evolutionary process. Its provide the single time
cost estimation results. Refinement cost
estimation procedure everything its possible
using portfolio techniques. The above
techniques are gives the better evidence based
cost estimation techniques of content. Here we
are show comparison results in between
previous
and
present
approaches
in
implementation.
II. Related Work:
Software effort estimation literature
appeared from last three to four decades. All
Successful projects are depends on effort and
schedule. Previously different computational
intelligence techniques are implemented for
software effort estimation. Those techniques are
fuzzy logic, neural networks and genetic
programming. The above techniques are
provides efficient results in software estimation.
Sometimes these techniques provide the results
are inaccurate efforts.
Fig1: Effort Estimation Planning
Firstly using dependent variables
estimate the cost and effort values specification.
These variables cost value is not sufficient apply
the adjusting factors. All variables values are
substitute and provide the efficient effort values.
Adjusting factors provides re-scalable solution.
Many times we are applying the adjusting
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factors and give the modified effort results.
When we are provide the accurate solution no
expert is not contains the idea. It is not provide
optimal solution.
Next apply the Function point. Its works
based on project size. In project size start the
estimation of cost procedure. It is not provide
the satisfiable and accurate solution. Next in
early stages based on lines of code (LOC)
performs software cost estimation models. This
approach also is not providing the optimal
solution. Using FP and LOC start the estimation
of cost models. These models are not gives the
proper solution.
Some other approaches are implemented
for software cost estimation like algorithmic and
non algorithmic environment. Algorithmic
environment is manual rules estimation
procedure. Sometimes it may take the wrong
decisions. Compare to algorithmic, non
algorithmic environments provides the best and
efficient solution. Non Algorithmic environment
itself applies the experienced and expert
solution. In this present environment sometimes
it may chance to apply the best opinions. After
some number of days apply the expert judgment
methods for software cost estimation. All
different kinds of issues it’s possible to control
using expert opinions.
Software cost estimation works based on
some data mining techniques. Data mining
techniques also its possible estimate cost in
efficient manner. Those data mining techniques
are classification and decision trees. These data
mining techniques give the solution under
approximate software cost estimation process.
Approximate cost analysis we are identifies
based on rough set analysis. No such data
mining techniques are not provides efficient cost
estimation. In independent variables it’s not
gives the proper estimation results. Now in new
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estimation techniques it applies the genetic
algorithm. All variables are communication
variables. It’s may possible to estimate the cost
somewhat efficient in implementation process.
Some kind of new investigation process
it’s possible to relate to object oriented design.
Using object oriented design it’s possible to
estimate somewhat efficient. Object oriented
design process uses as a reusable for
identification of improved cost estimation.
Neural
networks
and
artificial
intelligences are applied in software cost
estimation process. In software cost estimation
many number of layers are presented here.
Verification of all the layers and provide the
better results synchronization it’s very difficult
and complex. Synchronization results are
possible to identify based on learning
algorithms.
The above all techniques are not gives
the proper cost estimation solution in
implementation. It does not provide the perfect
estimation information in software cost
estimation process.
Some other intelligence techniques are
placed in implementation technique. Particle
Swarm intelligence we are applied in
implementation. Software Cost estimation
approach applies iteratively and adding the new
requirements. Its gives good software cost
estimation techniques results. Negative software
cost estimation converts into positive cost
estimation process in implementation. These
many numbers of times changes the
requirements users are feel like complex or
burden.
III. Problem Statement:
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Previous many number of data mining
techniques applied for software cost estimation.
Many number of previous techniques are not
update the software cost estimation parameters.
Using limited number of software cost
parameters provide the quality specification at
most 50%. There is no new estimation
parameters addition process in implementation
process.
Using
exhaustive approach
and
refinement subset mechanism update the
software cost estimation parameters. After
updating of all efficient cost estimation
parameters its provide the quality estimation
results. All unnecessary estimation parameters
are removed in implementation process. In each
and every iteration apply the pruning techniques
in implementation process. Group of group
estimation parameters gives the nearest cost
estimation optimal results. It is identifies from
different analysis specifications mechanism. All
groups of estimation parameters are quality
estimation parameters.
IV. Creation Intelligent Models for cost
estimation using Clustering techniques:
Many numbers of clustering techniques
are in training projects environment. Using
training projects identifies the new projects cost
estimation is very easy. In total projects
automatically categorize the number of projects
based on training projects. Its gives the
estimation results as a approximate manner
specification process. Previously k-means
clustering algorithm, k-mediods algorithms are
implemented in software cost estimation. It’s not
gives the proper cost estimation results. Next we
are uses the Partitioning around mediods
clustering techniques are preferred in software
cost estimation. This type of clustering
techniques applies exhaustively till gets the
optimal solution. This estimation process applies
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hierarchically for showing the efficient results.
In each and every step identifies the some kind
of subset of results. Using from starting of the
subset apply the refinement process. Perform the
different iterations and gets the effectiveness
subset results in output specification.
and provide the better improved cost efficient
effort estimation. In unsupervised mechanism
learn the some new things and add into previous
estimation approaches. These kinds of
approaches are applied till quality cost effort
estimation process. Using software cost
estimation provides the quality project
implementations. It’s not giving the quality
learning results then we are apply the navies
Bayesian technique. It’s is classify the
estimation parameters are positive and negative
preferences.
In present software cost estimation
mechanisms identifies the mean, variance and
standard deviation values specification. Is there
any values are available as a negative converts
into positive environment with new cost effort
estimation techniques. Every time changes the
pattern based on project, it’s very complex for
engineers in estimation environment.
4.2Pattern Approaches and Specific format
Approaches:
Fig 2: Clustering techniques based software cost
effort estimation process
Using K-means algorithm find out
similar projects related to group, training
projects are provides the good estimation
parameters. K-means provides complete training
results. Using those training results apply the
testing approaches in implementation.
4.1Noisy Handling approaches for intelligent
environment:
In previous software cost effort
estimation any errors are generate, its shows the
noisy problems, those noisy results in effort
environment implements with unsupervised
clustering techniques. Using new unsupervised
techniques adding the some rules for analyze
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Now we are introduces the new patterns
for
detection
of
software
estimation
mechanisms. Pattern based approach works for
all kinds of projects in implementation process.
Once we are define the software cost estimation
parameters, no need to changes the software
effort parameters. These pattern approaches give
the optimization in implementation. It’s not
providing the optimal solution then we are
implement generational evolutionary algorithm.
All software cost effort estimation
parameters identifies based on rating
approaches. In estimation approaches which
kind of variables are uses in all estimation
environment we are identifies. In total number
of variables select one by one variable, and gives
the rating. After utilization all software cost
estimation models generate the one new generic
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model. Using this generic model start the
estimation process. It is give proper solution
alignment process.
6. At last it’s providing the best variables
selection process related software cost
estimation.
4.3Algorithm: Software cost effort estimation
using generational evolution algorithm:
In new generational evolutionary process applies
the concept of portfolio management
environment. Previous variables are not
efficient, every time estimate the new variables
also in implementation process.
First here we are verifying different
model of estimation variables. After all models
variable analysis process find out the best
variable selection. In how many models same
variables are used find out based on similarity
function. Using similarity function value
specification finds out each and every variable
fitness value. Using fitness values based
variables select best variables. According to
fitness values gives the specification of rating.
These rating of variables are not sufficient
automatically new variables also possible to
evaluate and update the rating process. Its gives
good evidence of software cost effort estimation
results. This is completely related to
generational evolutionary approach. The above
all steps place into pseudo code.
V.Performance Evolution:
Fig 3: table values related k-means and k –
generational evolutionary process
1. From different models start the analysis
process and apply the similarity function
– each and every variable of similarity
variables results we are provide here.
2. This process applies till variables fitness
values. This process applies till
termination of all variables fitness
values calculation.
3. After calculation of fitness
provide the rating information.
value
4. Rating variables start the allocation
using reproduction operation.
5. Reproduction of variables is not gives
the sufficient content then applies the
replacement procedure.
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Fig 4: performance calculation in between of
k-means and generational evolutionary
process.
VI.Conclusion:
Compare present traditional clustering
techniques to new generational evolutionary
techniques are provides the best solutions in
software cost effort estimation environment. Its
can gives one kind good evidence. These things
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are efficient. Dynamic software cost estimation
models we are implement using portfolio
selection technique. Compare to previous
techniques present techniques are works as a
efficient techniques we are show the output.
VII.References:
1. Ziauddin, Shahid Kamal Tipu, Shahrukh
Zia, An Effort Estimation Model for
Agile Software Development, ACSA,
2012
2. J.N.V.R.Swarup Kumar, A Novel
Method for Software Effort Estimation
Using Inverse Regression as firing
Interval in fuzzy logic, IEEE.2011
3. Increasing the accuracy of software
development effort estimation using
projects clustering, IET, software, 2012
4. Arturo Chavoya, Applying Genetic
Programming for Estimating Software
Development Effort of Short-scale
Projects, IEEE, 2011
5. Dirk Basten and Werner Mellis, A
Current Assessment of Software
Development Effort Estimation. IEEE,
2011
6. A.BalaKrishna
,
FUZZY
AND
SWARM
INTELLIGENCE
FOR
SOFTWAREEFFORT
ESTIMATION,AITM, 2012
7. Surendra Pal Singh ,A Review of
Estimating Development Time and
Efforts of Software Projects by Using
Neural Network and Fuzzy, ijarcsse,
2012
8. Ziauddin ,An Effort Estimation Model
for Agile Software Development,
ACSA, 2012
9. Efi Papatheocharous, Computational
Intelligence
in
Software
Cost
Estimation: Evolving Conditional Sets
of Effort Value Ranges
10. Jin-Cherng
Lin,
Automatically
Estimating Software Effort and Cost
using
Computing
Intelligence
Technique, CSCP,2012
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