The Success of software development is based on the developers involvement and the adapted software architectures. The software development activities are highly dependent one with another in all its phases. The software development activities and its dependency of the internal activities are influences the quality of the software. The Associative analysis of software development which is focused to determine to identify the potential developers in line with the software development models. The potential software developer is identified based on the cumulative weight of developers contribution on each phases and its activities weight. The activity relationships are presented as a unit matrix and the common phase activity is calculated to determine dependency between the phases in each module, projects as per the selected as per the selected software architecture. A neural network model is constructed and identifies the developers weight related to their interaction between one phase of the project to another on the same project. The occurrence relationship and its frequency values are obtained according to the number of activities involved by each developer in their entire development process. The weight values supported to identify the influencing factors of the developers in different models. It will leads to identify the potential developers, future project planning and business planning activities.

1. Integrated Intelligent Research(IIR) International Journal of Business Intelligent
Volume: 04 Issue: 02 December 2015,Pages No.107- 111
ISSN: 2278-2400
107
Associative Analysis of Software Development
Phase Dependency
N.Sasikala
Associate Professor, MSCAS, Chennai
Abstract- The Success of software development is based on
the developers involvement and the adapted software
architectures. The software development activities are highly
dependent one with another in all its phases. The software
development activities and its dependency of the internal
activities are influences the quality of the software. The
Associative analysis of software development which is focused
to determine to identify the potential developers in line with
the software development models. The potential software
developer is identified based on the cumulative weight of
developers contribution on each phases and its activities
weight. The activity relationships are presented as a unit matrix
and the common phase activity is calculated to determine
dependency between the phases in each module, projects as per
the selected as per the selected software architecture. A neural
network model is constructed and identifies the developers
weight related to their interaction between one phase of the
project to another on the same project. The occurrence
relationship and its frequency values are obtained according to
the number of activities involved by each developer in their
entire development process. The weight values supported to
identify the influencing factors of the developers in different
models. It will leads to identify the potential developers, future
project planning and business planning activities.
I. INTRODUCTION
Software engineering is the process to attain the quality of
the software development to meet the desires and requirements
of the users in a business scenario. The field of computer
science especially on software development activities, it is a
tool to ensure to meet the standards of the quality software
development. The performance of the software developers are
significantly varies from one firm to another, because of their
process, people, and technology. All the firms are focusing the
high quality software but the delivery of the thing is less than
80% according to the NASCOM survey. Its because of the
unskilled developers, improper process, working towards the
development without adopting architecture and the deficiency
in software development phase activities. Therefore the
software architecture is highly commendable the quality
software mapping of the developers skills with the
development technologies and the maximum productions with
minimum error.This paper aimed to identify the potential
developer, using the dependency weight analysis process. The
Software architecture is defined in terms of structural software
development elements and its relationships. The first part of
the work analyze the existing software development models,
associative relationship between the phase activities and
identify the functional development activities, the weight
values to identify the potential developers. The scope of the
paper, related literature review, methodology, the experimental
results and its analysis are presented along with the possible
future work in this paper.
II. SCOPE
The research work is focused to map the dependency activities
between the software development activities, compute the
associate weight values, developers contribution and determine
the potential developers. The scope of this work attained with
the following objectives:
a) Identify the software development phase activities
according to each development architecture.
b) Identify the activities relationship in and between the
phases of the same project.
c) Construction of associative activities relationship matrix
d) Computation of phase weight with individual phases and
its relative phases.
e) Formation of neural network model to compute the
interactive weight in all possible associative matrix
connectivity.
f) Analysis of weight values for the identification of
potential developers.
III. METHODOLOGY
The predication of developer’s performance on different
software models designed and executed with neural network
back propagation algorithm. The research path is described
below
Step 1: Identification of development activities in existing
software models
There is several software development models used to develop
and deliver the quality products. As per the basic study of the
software engineering approach water-fall models, spiral
models, V models and iterative models are identified as mostly
used and traditional development models. These models have
the phase based approach as common but the numbers of
phases are differing according to the models. The basic
standard weight for each phase is assigned from the mostly
used common weight. The independent, dependent activities
are defined and the associative relationship matrix is formed.
Step 2: Identify the activities relationship in and between
the phases of the same project
The development model phases and its few activities are
selected to design the general phase and the activities of the
development model. The relationship between the activities of
one phase with the activities of next phase is identified. The
matrix was found with the values of ones and zeros.

2. Integrated Intelligent Research(IIR) International Journal of Business Intelligent
Volume: 04 Issue: 02 December 2015,Pages No.107- 111
ISSN: 2278-2400
108
Step 3 : Construction of Associative activities relationship
matrix
The step two calculations are given in three levels. The
relationship of each activity in the internal phase and the
successive phases are considered to construct the ARM in first
level. The successive phase dependent and forward, backward
relationships are used to construct the adjacent matrix in each
model. In second level the initial assigned weights’ are
distributed to the number of activities of each phase. According
to the calculated individual activity, the dependent and
independent level of each phase is calculated. The obtained
ratio is evaluated with regression and correlation to determine
the efficiency of the model. The third level the four
developments models are analyzed and identified that the
phases are not similar in all the models. Therefore the common
phase model is derived with seven phases and the activities are
identified. The system study, system analysis, system design,
coding, testing, implementation and maintenance phases are
identified as a common phases which the whole is set of the
entire analyzed model.
Step 4: Computation of phase weight with individual
phases and its relative phases
The observed data sets which consist of above mentioned
seven phases are selected for customization of each analyzed
models. All the seven phases are considered as a universal set.
The each model phases are considered as the subset of the
universal set. The waterfalls model consists of six phases.
These phase values are fetched in a customized manner from
the universal data set activities. In this model universal set
system study and system analysis phases are combined and
represented as analysis phase of waterfall’s model. Therefore
the universal set activities values of system study and the
system analysis activities values are merged and presented as
analysis active values of water fall’s model. The remaining
universal phase activities values are assigned to similar phase
activities of Waterfall’s model.
Step 5: Construction of NN Back propagation model
The observed and customized values are divided into two sets
namely developer’s performance and development activities
contribution. The phase cumulative contribution is consider as
a Input phase value and the developers five performance
factors which mentioned above are treated as a Hidden layer
for the neural network model. The performance and
contribution level of each model is assigned as an output layer.
The neural network model is constructed with 6 or 5 or 4
phases as an Input layer and 5 factors of performance as a
hidden layer and two attributes results are an output layer.
Such a way there are three neural network models are
constructed and trained with the universal data set.
Step 6: Developers Performance prediction
As per the trained network model of the universal set and the
customized data set of each module are trained. The hidden to
output layer weights are fetched with the optimized network
values. The maximum performance weight from hidden to the
output is compared and derived the best development model
for the developers. The obtained possible weights and the
maximum presentations are tabulated will presented in the next
section.
IV. CONSTRUCTION OF ACTIVITY
ASSOCIATION MATRIX
(2a)Activity relation
(2b) 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.
(2c)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.
(2d)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.
(3)Variable Assignment:
(1)Model specification:
Several variables are determined for the model, phase and
activities. For the water fall model the variables can be given
as follows.
PxWA -> W is a variable assigned to represent the waterfall
model.
(2)Phase Specification:
The variable assignments can be done for different phases
of each model. If we take the different phases of the water fall
model than the variables are:
Feasibility study phase (P1wA)
P1Wa1 -> client study
P1Wa2->Process study
P1Wa3-> find best process
Requirement Analysis phase (P2wA)
Requirement gathering -> P2Wa1
Requirement analysis -> P2Wa2
Aw requirement -> P2Wa3
4. Activity weightage calculation
(a) Individual phase weightage
Number of activity in phase/total activities of model *
Feasibility study => 3/ 19 * 10/100=0.1579*0.1
=0.01579
(b) Individual activity weightage
Weightage per activity = Individual phase weightage
0.01579
_____________________ = ___________
= 0.0053

3. Integrated Intelligent Research(IIR) International Journal of Business Intelligent
Volume: 04 Issue: 02 December 2015,Pages No.107- 111
ISSN: 2278-2400
109
Number of activities in
that phase 3
5. Relationship weight calculation
A) Construction of relationship between
Phases Px => Py
Activities of (p1)+activities(p2) * weight % of p1 +
weight % of p2
--------------------------------------------------------- ---------------------------------------
------------------------
Total of all phase total % of weight
3+3 *10+10=> 6 * 20
----- ----------- --- -----
19 100 19 100
=> 0.3158 * 0.2 = 0.0632
This is the total relation value of p1 & p2
Possible relation value = 3*3=9
Per activity relation between p1 & p2
----------------------------- ---
--------
Possible relation value 9
= 0.0070
b) Occurrence relationship of PxA=>PyA where A
is an existence
As per the association map table & water fall
model, 5 relationships occur between p1 & p2
Per activity relation value * 5 =0.0070 * 5
=0.035
The feasibility study phase possibility to affect in
the requirement analysis phase as follows
0.035 => 0.035 = 0.035
* 20
--------- ----------------------- ----------
-----
(p1+p2) 0.01579 + 0.01579 0.03158
100
=1.1083 *
0.2
Originally affected =0.2217
Dependency level of p1 + p2 = 0.2217
Independency level of p1 + p2= 1-0.2217
= 0.7783
(6) Construction relationship weighted matrix
In this step I am constructing the relationship weightage
matrixes between the phases x and x+1
(a)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
(b)Routed relationship function
(i) 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
(ii) 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.
(c)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 these 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 .
(d)nodes process as a weight
The weightage of the particular activities of a
particular phases are given as the value of the nodes
in layers of the network model. Example of
waterfall model network is given below:
0
0.5
1
1.5
2
Spiral Model
Iterative Model
V Model
WaterFall Model
Spiral Model 0.1871 0.2439 0.2758 0.3672 0.2222
Iterative Model 0.3352 0.6355 0.486 0 0
V Model 0.3035 0.3997 0.5335 0.5561 0
WaterFall Model 0.20088 0.2378 0.253 0.8389 0.4291
Phase1 Phase2 Phase3 Phase4 Phase5
Figure-1
Comparison of Dependency values of waterfall model within phases
WATER-FALL MODEL

4. Integrated Intelligent Research(IIR) International Journal of Business Intelligent
Volume: 04 Issue: 02 December 2015,Pages No.107- 111
ISSN: 2278-2400
110
V. CONCLUSION
The associative analysis of software development phase
dependency for the identification of potential developer
methodology is designed, executed and experimental results
are obtained. The existing architecture phase analysis, the
common phase such as design, analysis, development,
implementation and testing and maintenance. The development
activities are identified and represented in Associative
Relationship Matrix. As per the Associative Relationship
Matrix values the common phases are identified between the
architecture. The weight values are computed as per the
developers involvement, contribution, and their performance.
A neural network model developed and tested to identify the
iterative activities weight from one phase to another. Based on
the computed values potential developers are identified. This
developer’s could be assigned for future technology drive. The
model analysis selected the specific and limited number of
activities could be inserted and tested as part of the future
work.
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ISSN: 2278-2400
111
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