This document proposes a new approach for software project estimation that combines existing estimation techniques. It involves using case-based reasoning to retrieve similar past projects, reusing their estimates, and revising the estimates based on new parameters and delay-causing incidents. The approach allows parameters to be added dynamically during project execution to make estimates more context-sensitive and help converge to actual values. A prototype tool has been implemented to demonstrate calculating estimates by dynamically selecting parameters and computing similarity indexes between current and past projects.

1. A Project Planning tools for system management
ABSTRACTS
Several models of software estimation are available in the industry today.
Models are available for estimating project cost, schedule, defects, personnel
required etc. Available estimation models can be classified as being analogy
based, parameter based, expert based and size based. Experimental results show
that different models yield different results of estimates for the same project. In
this paper we demonstrate a technique that combines two available techniques.
Our approachpermits adding parameters for estimation dynamically based on
the context of the project using case based reasoning. These parameters are
used to compute a similarity index, which in turn, is used for estimation.
Estimates can also be revised based on delay causing incidents that occurduring
the execution of the project.
KEYWORDS
Estimation, Scheduling, Cost, CaseBased Reasoning, WBS, Capacity-Planning.
INTRODUCTION
The effort required to maintain a software work productor an enhancement
request can be estimated using a variety of models. The models can be broadly
classified as being parameter based or being based on expert estimation.
Parameter based models can be analogy based, size based or be combined. The
most common parameter based model namely: COCOMO. The function point
model is based on size. There is also widespread use of the expert method to
estimate the effort [Work Breakdown Structure (WBS)] required to develop
a module.
PROF. DR JAMILIN JAIS
INFRASTRUCTURE UNIVERSITY KUALA LUMPUR
jamilin@iukl.edu.my
[Cite your source here.]
MOHAMMAD OBYDUR RAHMAN
MASTER IN INFORMATION TECHNOLOGY
INFRASTRUCTURE UNIVERSITY KUALA LUMPUR
ID: 153915771 Email: obydur69@gmail.com
[Cite your source here.]

2. The COCOMO model uses discrete parameters about resources and developed.
A size based model uses size and productivity ratio for estimation. A size
based model would hence be most applicable when there are large team sizes so
that the skill level or the productivity ratio measurements are normalized. The
WBS uses an experiential method for estimation where there are no rules to
correlate a task or a sub task to the number of hours. The limitations of WBS
are that experts cannot have multi domain expertise. As different models would
work well under different scenarios, a combination based estimation approach
that combines the expert estimation model (WBS) with an analogy based
technique would make estimation more robust. We present a new methodology
for estimation that combines approaches from several different models. In our
approach, we use the standard parameters from various available methods along
with analogy based technique to provide context sensitive estimates. The
estimation methods found to be in most frequent use are expert judgment and
there is no evidence that formal estimation models lead to more accurate
estimates.
METHODS OF EXISTING ESTIMATION
The project involves a new internet based portal for recruitments.
Information about interviews conducted, interviewers, applicants are kept in a
MS-ACCESS database. In addition access should be provided via a web form
to update interview status. We present details regarding estimation of effort
required using three different approaches:using COCOMO model, model based
on function points and using WorkBreakdown Structure (WBS).
Estimation based on function points:
UFP = 120; LOC=5900; Productivity ratio = 10 tested lines/hour.
Effort required = 590 hours
Estimation based on COCOMO model:
Effort = (2.4) * (5.9) exp 1.05 = 120 hours
Estimation based on WBS:

3. Task Sub Task Estimate in
hours
Hardware Software
Maintenance
Backups 10
Disk format 24
Design Table collection 24
Primary Keys 8
Web page Design 24
Coding 24
Application testing Func 24
Non-Func 12
Deployment Local 30
Prod 10
Total 190
Table 1: Estimation based on WBS
It is obvious that the three methods yield different results for the same project.
Moreover, very frequently these estimates do not converge to actua . After
execution of project, it turned out that the actual effort was 40 man days or 320
hours and none of the above three models converged to the actual value.
These models gave different results due to usage of different types of
parameters. To summarize, the reasons why these models yield different results
and do not converge to actual values are as follows.
1. Using discrete parameters as opposedto many more parameters still not
discovered or is implicit. CBR will provide human like inferences.
2. Not accounting for real time schedule delay causing incidents that have
occurred in the near past.

4. 3. The estimates that are provided by parametric models are calibrated to
certain surrounding conditions that they have to be recalibrated.
4. These estimates are not context sensitive
NEW ESTIMATION APPROACH
We present a new approachfor estimation in this section. Our method for
estimation combines the three approaches in the following way:
 Well-known statistical approachfor calculating similarity index is used to
find projects in the past that are close to the project that is being
estimated.
 It includes casebased reasoning and uses the well- known R4 cycle
for the overall approach of estimation in order to derive inferences
from memory based estimations and revisions.
 Parameters can be added dynamically during the SDLC.
 Real Time delay causing incidents can be tracked which would make the
estimates more context sensitive and help converge to actual.
The highlights of our approachin addition to similar approaches described in
the literature is the flexibility to add parameters during the development life
cycle and to also revise estimates based on delay causing incidents that have
occurred in the recent past.
CASE BASED REASONING (CBR)
Case based reasoning is applied in this model to derive vital inferences that we
encounter and which are based on human judgment. Rule based systems can
mimic some problem solving strategies. CBR uses memory based problem
solving and reusing from past experiences. WBS uses a fair measure of our
intuition, only that the sequence of tasks is pre-defined. WBS can be integrated
with CBR in order to correlate with our memory rather than formulate rules.
The R4 cycle of the CBR comprises
 Retrieve the cases from the case-basewhose problem is most similar to
the new problem.
 Reuse the solutions from the retrieved cases to create a proposed
solution for the new problem.

5.  Revise the proposed solution to take accountof the problem differences
between the new problem and the problems in the retrieved cases. In
order to record reasons for such delays new parameters may be added
dynamically as and when required.
 Retain the new problem and its revised solution as a
New case for the case-baseif appropriate. As described earlier, our approach
allows the estimator to add parameters dynamically during the revise step.
During the estimation phase a project manager may intuitively use some
parameters which is not originally parameterized or is limited by discretization.
The set of parameters used during the Retrieve phase have to be modified
during the courseof project execution.
The addition of parameters resembling our own intuition and also reusing
estimates from past projects and other incidents that have immediately occurred
make the revise phase of the four pronged approach more context sensitive.
This will aid in the estimates converging to the actual.
PARAMETERS OF THE MODEL
There are two types of parameters listed here. The first set of parameters is
those that can be readily measured and quantified. Personnel attributes used in
parametric estimation models suchas the COCOMO cannot be exactly
quantified or in the case of another example a parameter such as “complexity of
requirements” can be better understood than measured.
Factors that can be easily measured and quantified using well- known
approaches are
 Number of simple requirements
 Number of complex requirements
 Design – Number of Internal/External Interfaces
 Design Complexity
 Reusability of design
 Size of the code
 Codecomplexity
 Reusability of Code
 Testing Efficiency
 Productivity Ratio
 Process Adherence

6.  Review Effectiveness
Parameters that can be easily understood rather than be measured are
o Experience level of the resources.
o Review effectiveness.
o Practice level of the resources.
o Domain knowledge.
o Familiarity with the work product.
o Time estimates of similar projects that have been executed in the past.
Similarity Index
Similarity index is computed using variance between the projects’ list of
parameters that are used for estimation versus those available in the database.
The formula below is used to calculate similarity index, between the project
that is being estimated and another project from the case data base that is
being considered for comparisonusing the underlying set of parameters.
Similarity Index = ∑ (Ci - Pi) 2,
Where
 i =varies over the number of parameters,
 C i = Value of the ith parameter of the project that is being
considered from the case data base, and
 Pi = Value of the ith parameter of the project being estimated.
Similarity index computes the absolute value of the difference per parameter.
For two projects wherein majority of parameters have values that are close to
each other, the similarity index will be closer to 0. In such a case, we conclude
that the projects are similar in nature and details regarding effort available in the
case data basecan be re-used for the project being estimated.
CASE STUDIES
We have implemented a proto-typetool that aids in computing estimates by
dynamically choosing and adding parameters that are used for measurement.
The tooluses a database to store parameters of projects that have already been
executed in the past. Features to calculate similarity index and display
parameters of matching projects from the case base are available in the tool. In
this section, we briefly present details regarding estimation for 10 projects

7. involving maintenance of application software that the first author was involved
in.. Foreach project, the table presents estimated data using WBS approach,
the actual effort and also effort estimated using our proposed approach.
CONCLUSION:
We have implemented a proto-typetool that aids in computing estimates by
dynamically choosing and adding parameters that are used for measurement.
However we have introduced an ongoing project called WEBCAP that
ultimately manages (reconfigure or redesign) Web resources for real time
multimedia document retrieval while satisfying all users and systems
constraints.
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