Knowledge Identification using Rough Set Theory in Software Development Processes

Integrated Intelligent Research(IIR) International Journal of Business Intelligent
Volume: 01 Issue: 01 June 2012,Pages No.11-14
ISSN: 2278-2400
11
Knowledge Identification using Rough Set Theory in
Software Development Processes
R. Rameshkumar, C.Jothi Venkateswaran,
Research Scholar, Bharath university, Salaiyur, Chennai
Head, PG and Research Department of Computer Science,Presidency College (Autonomous), Chennai
ramesh116@hotmail.com
Abstract- The knowledge processing system leads the
power of the organization in the world business race. All
the industries are adopting knowledge management system
for their human capital .The level of interaction occurs
among the employees in the industry increase the
knowledge creation, identification, representation and
utilization. The knowledge discovery data process
complexity various depend on the domain, nature of the
applications, organizational system and many more
organizational policies. The process time and volume of
data is to be reduced for the decision supporting and
Knowledge data discovery process using rough set theory
equivalence association in the software development
process and Information Technology Organization.
Determination of the target factor variables that influence
the processing knowledge in the organization .The
variables are identified based equivalence association of all
combinational factors of the variables. The researcher
paper observed software development project, which
produced un-deterministic result of the project
development. This paper aimed to find the relations of
variable, which could contribute more knowledge for the
successful completion and delivery of the project that
increase the software process development delivery.
However, the activity variables leads to determine the set
of activities carried out the professional group and
encourage them to provide more attention on the selective
activities.
Keyword: KDD, software development , variable
reduction
I. INTRODUCTION
Knowledge discovery in databases (KDD) is the process of
identifying needy information as a result of data
processing. The KDD approach is presented with the high
level conceptual manner [1] where it has been
decomposed in a few iterative steps. This approach is
attempted to impellent in the software development
process. Software development quality process is achieved
by using different methods to complete the entire activities
of the proposed project or product. To obtain the
fulfillment of the particular product, there are several
models used in the field of software engineering. Different
phases are involved in the development process, which is
common for the engineering module adopted for the
analysis. As a model, Waterfall model as simplest process
by finding the dependency between activities and
constructing a network for those dependency values with
the use of association matrices and map .This will throw a
light on the many relations and implications of the
waterfall model for a particular selected project. This paper
describes the dependency level analysis in reduction of
variables using data processing techniques such as rough
set theory.
II. BACKGROUND OF THE RESEARCH
There are various approaches and practices are adopted in
data mining process such as Classification, Regression,
Clustering, Rule generation, Discovering association rules,
Summarization, Dependency modeling and Sequence
analysis. There are many methods such as fuzzy logic,
neural network, genetic algorithms, genetic programming
and rough sets. Each of them can analyze a problem in its
domain, those methodologies can be used together to solve
complex problems, and more and more researches combine
those methods to find new critical features. Using the
above techniques and approaches in data mining it is easy
to find out a huge number of patterns in a database[1].
Rough set theory suits to analysis of different types of
uncertain data and rough set can deal with large data to
reduce superfluous information and find extracting
knowledge form the rules.Rough sets theory is developed
and applied in data mining and knowledge discovery
process [2,3,4,5,6]. It has been applied to the analysis of
many issues, including medical diagnosis, engineering
reliability, expert systems, empirical study of material data
[7], machine diagnosis [8], travel demand analysis , data
mining [9].The research addressed the effect of
attributes/features on the combination values of decisions
that insurance companies make customers’ needs satisfied
[10]. Rough set theory can unify with fuzzy theory and is

ISSN: 2278-2400
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transformed from the crisp one to a fuzzy one, called
Alpha Rough Set Theory. The rough sets theory is useful
method to analyze data and reduct information in a simple
way. This shows that rough set theory used for pre
processing of the data mining process, which leads the time
consuming and cost effective approach for the business
solutions. This approach is attempted to find the effective
attributes for the determination software developers
activity evaluation which set of attributes to be consider for
the quality software production and provide skill set to the
employees. The activities, which are carried out for the
different projects and its relational impact, are converted
using associative mapping process, which represented
below
III. CONSTRUCTION OF ACTIVITY
ASSOCIATION MATRIX (AAM) :
Activity Relation: Each activity in phase x is taken and
the impact of that particular activity in the next phase
(x+1)is considered. If the activity a1 creates an impact in
the next phase then that impact can be called as Activity
relation. These types of activities are also called as
dependent activities. If that particular activity a1 did not
create any impact in the next phase then it is called as
independent activity.
Independent activity association matrix
The Feasibility analysis set (P1a) is represented as column
and Requirement analysis set (P2a) is represented as row
and then a two dimensional association matrix is framed
with the following conditions.
Condition 1:
If the activities of Feasibility analysis set (P1a)
create an impact with the activities of Requirement
analysis set (P2a) then the value is set as ‘1’. These
activities are considered as related activities or dependant
activities.
Condition 2:
If the activities of Feasibility analysis set (P1a) does not
create an impact on the activities of Requirement analysis
set (P2a) then the value is set as ‘0’. These activities are
considered as isolated activities or independent activities
Condition 3:
If the activities of Feasibility analysis set (P1a), partially
creates some impact on Requirement analysis set (P2a)
then those activities to be called as partial dependant
activities. If its impact is dominant on the process then the
value to be considered appropriately to the dependant
activity else it is treated as an isolated activities.
In certain cases these partial activities are treated
as X (don’t care condition).the matix is given below:
Cyclic Avoidance: : The activities relationships are
determined only for the phase x with the next phase x+1.
There is no determination of activities within the phase
itself. So there is no cyclic path for the relationship
determination.
Multi level Relationship: : If there is a relation between
the phase x with phase x+2 through the phase x+1, then a
multi level relationships will occur.
Feed Forwarded approach: : The activities
relationships can be determined only for the phases x,
x+1,x+2 etc. as a forward approach. Here there is no
determination of relationships for the x+1 phase with its
previous phase x i.e. no backward relationships.
IV. ASSOCIATION MAP CONSTRUCTION
Direct relationship function : Association map is
constructed for the phases x and x +1 if there is a direct
relationship between them. If the phase x is having relation
with x+1 then there is a path existing between the x and
x+1 phase in the association map
Routed relationship function :
Single hidden phase : This type of routed relationship is
constructed if there is a path between the phases X and the
phase x+2 through the phase x+1 which is in between
those two phases
Multilevel hidden phase : This type of relationship occurs
only when there is a relationship between x and X+3 or
x+4 phase which is having the path existence through x+1
and x+2 etc.
Network construction
Now the network is constructed from the association map
by the following steps :
(a)Phase as a Layer : In this network construction we
are considering the phases as layers. Since the waterfall
model has 6 phases and so those phases are considered as 6
layers of the network construction.
(b) activities as a node
Each activity of the phases is considered as the nodes of
the network construction. In the phase1 of waterfall model
there are three activities and those three are going to be
considered as nodes of the first layer of the network.
Likewise the remaining phases and their activities are
treated as the respective layers and nodes .

ISSN: 2278-2400
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As per the constructed network model the associative
relationship matrix are constructed. While observing the
activities of the employee and their activities the
contribution of employee and their each activity in the
software development phases presented as a unit matrix.
This matrix representation has seven phases and each
phase four activities are consider for the evaluation. The
employee activities on these phases along with the
performance observed and presented.
V. RESULT INTERPRETATION
The development environment is observed and the sample set of data is partially presented according to the observed
performance. The numbers of activities, which are involved as per the involvement the developer performance is, vary
one with another. The collect data sample presented below
sno Emp.id Regularity
Task
completion accuracy
Team
Involvement Reporting Performance
1 1000 100 82 98 84 90 90.8
2 1004 80 93 82 93 86 86.8
3 1007 92 91 90 81 90 88.8
4 1011 100 93 95 92 80 92
5 1013 86 95 100 92 93 93.2
6 1015 87 80 99 80 97 88.6
7 1019 99 81 89 95 95 91.8
8 1024 91 80 96 94 90 90.2
9 1028 82 99 87 89 88 89
10 1030 93 87 81 99 98 91.6
11 1122 89 90 90 80 83 86.4
12 1128 82 97 90 84 81 86.8
13 1133 100 100 99 95 82 95.2
14 1140 88 82 98 80 85 86.6
15 1148 94 87 98 89 91.8
16 1152 98 87 83 96 90 90.8
17 1161 83 98 83 87 83 86.8
18 1166 88 100 98 97 82 93
19 1175 82 97 92 80 91 88.4
20 1182 80 84 99 88 95 89.2
If the activities and the skills set is identical. According
to the relational activities and the skill employee
performance are differ one with another.
I. CONCLUSION
These papers address the developers skill set and the
performance according to the contribution of
development phases. These developmental phase
activity performance are differing one with another
based on the domain and the skill set. In the internal
phase activities are consider high determining factor for
the developmental activity .While evaluating the
performance the performance factors are differ as per the
number of phases and the skill set of the employee. This
concludes that the business environment evaluation and
industrial service could be dome through the analysis of
employee involvement in the software development. The

ISSN: 2278-2400
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roughest theory implementation carried out as proposed
model of determination of variable set.
As per the number of activities in the different phases
and their performance the chart is presented below.
REFERENCES
[1] Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, and
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[8] Zhai, L.Y., Khoo, L.P., and Fok, S.C. 2002, “Feature extraction using
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[9] Li, R., and Wang, Z.O. 2004, “Employees’ behaviors,”European
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[10] Grzymala, J. and Siddhave, S. Rough set Approach to Rule Induction
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[11] Wang, X., Yang J. Teng X., Xiang, W., Jensen, R., Feature selection
based on Rough Sets and particle swarm optimization. Pattern
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