International Journal of Advancements in Research & Technology, Volume 2, Issue 5, M ay-2013
ISSN 2278-7763
312
The Comparative Study of Software
Maintenance Forecasting Analysis Based
on Backpropogation (NFTOOL and NTSTOOL) and
Adaptive nuero fuzzy interference system ANFIS.
Anuradha Chug¹ and Ankita Sawhney²
¹ University School of Information and Communication Technology, GGSIP University, Dwarka, New Delhi 110077
² Computer Sc Department, University School of Information and Communication Technology, GGSIP University,
Dwarka, New Delhi 110077
a_chug@yahoo.co.in
ankitasawhney1@gmail.com
Abstract- Software could be called as heartbeat of today
technology. In order to make our software pace with the
modern day expansion, it must be inevitable to changes.
Enhancements and defects can be stated as two major
reasons for software change.Software maintainability
refers to the degree to which software can be understood,
corrected or enhanced. Maintainability of software
accounts for more than any other Software engineering
activity. The aim of the paper is to present a study based
on the experimental analysis to show that ANFIS can
more accurately predict maintainability as compared to
other models. Predicting the maintainability accurately
using ANFIS has got its benefits in now a day’s risk
sensitive business with economic environment where
software holds the most valuable assets ,may it be
transaction processing (governmental or banking service)
,manufacturing automation(chemicals ,automation) or
any other industry.
phase is required to keep the software up to date with
the environment changes and changing user
requirements. The maintenance phase can be defines as:
Maintainability: The ease with which a software can be
modified in order to isolate defect or its cause, correct
defect or its cause, cope with a change environment or
meet new requirements.
A good quality, maintainable software is always based
on robust design. We can design a good design as one
which allows easy plug in of new functionality in terms
of new methods and new classes without
reimplementing the previous iteration results. In order
to calculate maintainability we first need to calculate
the change effort, which deals with the average effort
required to modify, add or delete a class. Maintenance
could be regarded as the most important phase as it
consumes as it consumes about 70% of the time of a
softwares life and about 40-70% of the entire cost of
software is spend on the maintenance of the software
[1].
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Keywords-ANFIS (Adaptive nuero fuzzy interface
system), ANN (Artificial nueral network), Software
Maintainability, OO metrics, change, S.D (Standard
Deviation), MSE (Mean square error)
.1. Introduction
Software systems after their release are subjected to
continuous evolution and modification. Software
includes many phases; maintenance is the last of all the
phases .This phase is initiated after the product has been
released. The maintenance phase of the software is the
most expensive and time consuming of all the phases
amongst its life cycle, and because of this it remains a
major challenge in quality engineering studies [1]. This
Copyright © 2013 SciResPub.
The maintenance process can be categorized in the
following four areas of interest [2]:
a) Corrective maintenance: Modification of software
which is performed after the delivery to correct the
discovered problems. Examples of corrective
maintenance include correcting a failure to test for
all possible conditions or a failure to process the last
record in a file.
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International Journal of Advancements in Research & Technology, Volume 2, Issue 5, M ay-2013
ISSN 2278-7763
b) Adaptive maintenance: Modification of a software
performed after the delivery so that the software
product remains usable in a changing or changed
environment. Examples of adaptive maintenance
include implementation of a database management
system for an existing application system and an
adjustment of two programs to make them use the
same record structures.
c) Perfective maintenance: Modification of a software
performed after delivery so as to improve the
performance
or maintainability.
Examples
of
perfective maintenance includes modifying the
payroll program to incorporate a new union
settlement, adding a new report in the sales analysis
system, improving a terminal dialogue to make it more
user-friendly, and adding an online help command.
d) Preventive maintenance: Modification of a
software performed after the delivery such that the
latent faults get detected and corrected before they
become effective faults. Examples of preventive
change include restructuring and optimizing code
and updating documentation.
Many studies have been conducted in past which have
found a strong link between the object oriented metrics
and software maintainability and that maintainability
prediction can be done using these metrics[3]. The
Objective of our research is to apply ANFIS tool [4]
along with the two other tools, all based on Nueral
networks, compare them and ascertain their
performance in terms of software maintainability.
For this we initially studied various metrics available
and amongst them selected those which showed strong
relationship to maintainability [5]. The metrics which
were chosen by us for the analysis includes size,
weighted methods per class, depth of inheritance tree,
number of children, number of methods and coupling
between objects. Then we divided the available data in
three sets of ration 3:1:1 and used them for training,
testing and validation.
313
effect of metrics also depends upon the development
environment, programming language and the
application. I W., Henry S. [3] used A Classic Ada
Metric Analyzer in the year 1993 for predicting
maintainability. In the year 1993 Rajiv D Banker et al
[6] also predicted maintainability based on Statistical
Models which assigned weights to each metric used.
In year 2002 K.K. Aggarwal et al [7] predicted software
maintainability prediction based on fuzzy logic. They
took into consideration three aspects of software to
measure maintainability which was readability of
source
code,
documentation
quality
and
understandability of software. The advantages of using
new rules base of fuzzy models were that they required
less storage space and would be efficient when used to
find the results in simulation. In year 2003 two
predictibilty models were made one by Melis Dagpinar
et al based on Correlation, Multivariate Analysis, Best
subset Regression Model and the other by Stamelos et
al based on Bayesian belief networks. In year 2005
Thwin et al [8] predicted the software development
faults and software readiness using general regression
nueral network and ward nueral network. In their
research they performed two kinds of investigationsprediction of number of defects per class an prediction
of number of lines changes per class.
In2008 N.Narayanan Prasanth et al based their analysis
on Fuzzy repertory table and Regression analysis. They
took into consideration sample of 4 products and
calculated both absolute and relative complexities over
it. Then using fuzzy repertory table technique they
acquired the domain knowledge of testers and used it
for software complexity analysis. Regression analysis
was then used to predict maintainability from code
complexity of product. In the year 2011 Marian
Jureczkoused Stepwise logistic regression to predict
maintainability.
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2. RELATED WORK
There are several models which have been proposed to
predict the software maintainability. These models
varies from simple regression analysis models to
complex machine learning algorithms .Some of the
existing works are listed as below :
G. M. Berns used maintainability analysis tool which
is a kind of lexical analyser to predict maintainability
in the year 1984.D Kafura et al used static analyser as
a count to predict maintainability in year 1987.They
used seven different metrics to predict maintainability.
For their study they took three different versions of the
system and worked on it. Wake, S. et al used Multiple
Linear Regression Model in year 1988 to predict
maintainability. They used multiple metrics as
parameter for their model and also showed that the
Copyright © 2013 SciResPub.
3. METRICS STUDIED
Software metric is a measure of some property of a
piece of software or its specifications [5]. To study the
various features of object oriented languages which
includes inheritance, encapsulation, data hiding,
coupling, cohesion ,memory management we require
careful selection of metrics. Object oriented metrics can
be used throughout the software life cycle to quantify
different aspects of an object oriented application. We
studied various metrics and amongst those we have
selected those which show strong relationship with
software maintainability.
The following OO metrics described in table 1 have
been taken as independent variables in our study:
TABLE 1
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International Journal of Advancements in Research & Technology, Volume 2, Issue 5, M ay-2013
ISSN 2278-7763
INDEPENDENT METRICS
DEFINITION
Metrics
SIZE
1(Lines of
code):
WMC
(Weighted
Methods
per Class):
DIT (Depth
of
Inheritance
Tree):
NOC
(Number of
Children):
Definition
The number of total lines of code
excluding the comments.
NOM
(Number of
Methods):
CBO
(coupling
between
object):
The number of methods in a class.
The sum of McCabe’s cyclomatic
complexities of all local methods in a
class.
The depth of a class in the inheritance
tree where the root class is zero.
The number of child classes for a
class. It counts number of immediate
sub classes of a class in a hierarchy.
CBO of a class is a count of the number
of other classes to which that class
is coupled.
varying in size and application domains were collected
from different open source software progress. In our
study we collected and analyzed 25 such classes.
CCCC tool was used to analyse the codes and create a
report on various metrics of the code. CCCC is a free
software tool for measurement of source code related
metrics by Tim Littlefair. Some of the advantages of
using CCCC as a metric analyser are: It contains
internal database recoded using STL which makes it
faster, It has got persistent file format which allows
analysis of outcomes to be saved across runs, reloaded,
read by other tools, Using CCCC all the output files
are generated into a single directory, (by default .cccc
under the current working directory), Each class
identified by CCCC has a detailed report attached with
it. In report, low-level data are accompanied by HTML
links to the location in the source file which gave rise
to the data and configuration directory is no longer
required.
Six different metrics calculated using CCCC for 25
sample classes are Size1, weighted methods per class,
number of children, coupling between object, number
of methods, and depth of inheritance tree.
The dependant variable change is calculated by
keeping following rules in mind.
a. Any new comment in line in the new version of the
software is not counted as an added line.
b. If any two consecutive lines of the previous version
are merged as one single line in the recent version
then it is regarded as one deleted line change and
one modified line change.
c. Addition of any new lines to the source code and
addition of curly braces which were not there in the
previous version and have been added in the recent
version of the software are counted as added lines
change.
d. Deletion and addition of blank lines in not
considered as a change.
e. Reordering the sequence of a line is not considered
as a change.
f. Deletion of the statements, curly braces, lines of
code which were present in the previous version
and are no present in the recent version are
regarded and counted as deleted lines.
g. Any change or variation in the lines of comment is
not considered a change.
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Forecasting the maintenance of the software could help
in better enhancements at a lower cost. In our study we
propose to investigate relation between object oriented
metrics and change effort, which indirectly reflects the
maintainability of the software being developed. The
dependant variable therefore used in our study is change.
The dependant variable metric change has been
described in TABLE 2:
TABLE 2
DEPENDENT METRICS DEFINITION
Metrics
CHANGE
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Definition
The number of lines added and
deleted in a class, change of the
content is counted as change.
4. EMPERICAL DATA
In this section we will introduce our data sources and
will provide description about the data collection
method. Two different versions of C++ source codes
Copyright © 2013 SciResPub.
The data set was than divided in the ratio of 3:1:1 for
the training data: testing data: validation data means
60% of the sample data is used for training that is
using the specified algorithm machine learns from the
data patterns. The next 20% of the data is used for
testing to analyse how much the predicted and actual
values are close to each other. The last 20% of the data
is used for validating.
The samples were than trained according to the
appropriate algorithm selected.
The table 3 shows the descriptive statistics of the
various data metrics used.
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International Journal of Advancements in Research & Technology, Volume 2, Issue 5, M ay-2013
ISSN 2278-7763
TABLE 3
STATISTICS OF DATA SET
Metric
Mean
S.D
Suppose, for instance, that we have data from a housing
application and we want to design a network which can
predict the value of a house given pieces of
geographical and real estate information. We have a
total number of homes for which we have items of data
and their associated market values.
We can solve this problem in two ways:
a. Use a graphical user interface, NFTOOL, as
described in “Using the Neural Network Fitting Tool”.
b. Use command-line functions, as described in
“Using Command-Line
SIZE1
Minim
um
5
1040
281.16
293.
9
WMC
1
24
6.64
5.51
DIT
0
1
0.12
0.33
NOC
0
1
0.16
0.37
CBO
0
2
0.32
0.56
NOM
1
24
5.88
5.80
The number of hidden neurons selected for our sample
data: 10
After the collection of data we then used Nueral
network toolbox to predict maintainability and validate
the results. Following standard steps are followed while
designing neural networks to solve problems
application areas:
The structure of nueral network and the training
algorithm selected in NFTOOL inside nueral network
toolbox is as shown in figure 1
a.
b.
c.
d.
e.
f.
g.
Maximum
315
In our study we are working with the graphical user
interface.
To work with the fitting problem we first arrange a set
of input vectors as columns in a matrix than arrange
another set of target vectors (that is the correct output
vectors for each of the given input vectors) into a
second matrix.
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Collect data.
Create the network.
Configure the network.
Initialize the weights and biases.
Train the network.
Validate the network.
Use the network.
At the end the results obtained from all the three
different techniques obtained based on the mean square
error value obtained(MSE).The mean square error could
be defined as the average square difference between the
outputs obtained and the targets. Lower value of MSE
implies better results and zero value of MSE depicts no
error.
5. PREDICTION OF MAINTAINACE USING
BACKPROPOGATION (NFTOOL)
Firstly maintenance effort prediction models are
constructed and the predictive accuracies are compared
with the actual values using NFTOOL available in the
matlab nueral network toolbox.
Using the NFTOOL [10] we train the network to fit the
inputs and targets. It solves the data fitting problem
with a two layer feedforward network trained with
Lerenberg-Marquardt.
Neural networks are good at fitting functions. In fact,
there is proof that a fairly simple neural network can fit
any practical function
Fig 1 :( Snapshot of trained nueral network using
NFTOOL)
The Mean Square Error values (MSE) values observed
for the above system are listed as below in table 4.
TABLE 4
RESULTS OBTAINED FOR NFTOOL
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International Journal of Advancements in Research & Technology, Volume 2, Issue 5, M ay-2013
ISSN 2278-7763
Data
Training
Testing
Validation
No of samples
17
4
4
316
MSE
8.98e-0
28.26e-0
33.53e-0
A plot of comparison between training, testing and
validating data is shown in figure 2:
Fig 3: Snapshot of trained nueral network using
NTSTOOL
The Mean Square Error values (MSE) values
observed for the above system are listed as below in table 5.
TABLE 5
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RESULTS OBTAINED FOR NTSOOL
Fig2: comparison between trained, tested and
validated values
6. PREDICTION OF MAINTAINACE USING
BACKPROPOGATION (NTSTOOL)
The next investigation is to construct maintenance
effort prediction models and compare the predictive
accuracies with the actual values using NTSTOOL
available in the matlab nueral network toolbox.
NTSTOOL stands for neural network time series tool.
Time series can be defined as a series of data where
the past values in the series may influence the future
values. (The future value is a nonlinear function of its
past m values)
NTSTOOL opens the nueral network time series
wizard and leads you through solving a problem. This
tool takes the past values and predicts the future values.
NTSTOOL was introduced in Neural Network
Toolbox version 7.0, which is associated with release
R2010b.
In order to train the data using NTSTOOL, a set of
input vectors are arranged as columns in a cell array
then the target vectors (that is the correct output vector
for each of the given input vectors) are arranged into a
second cell matrix.
The number of hidden neurons selected for our sample
data: 10
The structure of nueral network and the training
algorithm selected in NFTOOL inside nueral network
toolbox is as shown in figure 3
Copyright © 2013 SciResPub.
Data
Training
Testing
Validation
No of samples
17
4
4
MSE
262.13e-0
272.84-0
349.53e-0
A plot of comparison between actual and predicted
maintenance effort on validation data is shown in fig
4:
Fig4: Comparison between trained, tested and
validated values
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International Journal of Advancements in Research & Technology, Volume 2, Issue 5, M ay-2013
ISSN 2278-7763
7. PREDICTION OF MAINTAINACE USING
ADAPTIVE NUERO-FUZZY INTERFERENCE
SYSTEM (ANFIS).
The next investigation is to calculate maintenance
effort using nuero fuzzy technique .ANFIS are a class
of adaptive network that are functionally equivalent to
fuzzy interference systems, they use hybrid training
algorithm. The ANFIS approach learns the rules and
membership functions from data.
ANFIS is an adaptive network. An adaptive network
is network of nodes and directional links. Associated
with the network is a learning rule - for example back
propagation. It’s called adaptive because some, or all,
of the nodes have parameters which affect the output
of the node.
These networks are learning a
relationship between inputs and outputs
Using a given input/output data set, the toolbox
function ANFIS constructs a fuzzy inference system
(FIS) whose membership function parameters are
tuned (adjusted) using either a backpropagation
algorithm alone, or in combination with a least squares
type of method. This allows your fuzzy systems to
learn from the data they are modeling.
The Matlab Fuzzy Logic Toolbox function that
accomplishes this membership function parameter
adjustments called ANFIS. We can start ANFIS using
either command line option or using graphical user
interface. In our study we have worked on graphical
user interface for ANFIS.As a initial step we load the
data(training ,testing ,validation ) using the appropriate
radio buttons. The loaded data gets plotted on the plot
region. We then either generate or load an initial FIS
model and then we select the FIS model parameter
optimization method-backpropagation or a mixture of
backpropagation
and
least
squares
(hybrid
method).after this the number of training epochs and
the training error tolerance are chosen and the data is
trained by clicking the train now radio button .We can
view the FIS output versus the training, testing and
checking data.
317
A plot of actual and predicted maintenance effort on
training data is shown in figure 5:
Fig 5 : Comparison between training and actual values
A plot of actual and predicted maintenance effort on
testing data is shown in figure 6:
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The Mean Square Error values (MSE) values
observed for the above system are listed as below in
table 6.
Fig 6: Comparison between testing and actual values
A plot of actual and predicted maintenance effort on
validation data is shown in figure 7:
TABLE 6
RESULTS OBTAINED FOR NFTOOL
Data
Training
Testing
Validation
No of samples
17
4
4
Copyright © 2013 SciResPub.
MSE
4.48e-0
15.65e-0
23.72e-0
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International Journal of Advancements in Research & Technology, Volume 2, Issue 5, M ay-2013
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Fig 7 Comparison between checking and actual values]
8. THREATS TO VALIDITY
This section included some of the limitations related to
the project. First, the object oriented classes are easier
to analyze till their complexity is below the threshold
level. The understandability difficulty increases as the
complexity increase. Second, The project runs well for
the small to medium scale projects but the matlab may
show an out of memory exception for the v large scale
projects for this we need to ensure sufficient memory
free space memory is available in the drive where
matlab has been installed.
9. CONCLUSIONS
In the work we have compared three different
techniques (Fitting tool, Time series tool and Adaptive
nuero fuzzy interference system) for predicting the
software maintenance effort (change) of software
system.
Comparing
the
figures(figure2,figure4,figure5,figure6,
figure7)
showing the difference between actual and predicted
values and the tables (table 4,table 5,table 6) showing
mean square error obtained from for three techniques
clearly shows that the mean square error value for
validation data obtained for ANFIS (23.7e-0)trained
network is less compared to the networks trained using
NFTOOL(33.53e-0) and NTSTOOL(349.53e-0) and
therefore predicted values obtained using ANFIS are
more closer to actual values in comparison to the
values from the other two methods (NFTOOL and
NTSTOOL).This concludes that results of ANFIS are
better for the prediction purposes. However, this
research has been done for a small system and
therefore the results needs to be generalized by
conducting similar analysis on data of other large real
time systems. In future we are planning to take other
paradigms apart from those taken in this study and
perform validation on it.
318
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ABOUT THE AUTHORS
REFERENCES
[1]Ruchika Malhotra
and Anuradha Chug,,” Software
Maintainability Prediction using Machine Learning
Algorithms”,Software Engineering: An International Journal (SEIJ) ,
Vol. XX, No. XX, 2012.
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[4]Jyh–Shing,Roger Jang ,”ANFIS-Adaptive network based fuzzy
inference system”,IEEE transactions on systems,man,cybernets
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Copyright © 2013 SciResPub.
Anuradha Chug is Assistant Professor in the
Department of University School of Information and
Communication Technology (USICT), Guru Gobind
Singh Indraprastha University, New Delhi. Her areas
of research interest are Software Engineering, Neural
Networks, Analysis of Algorithms, Data Structures
and Computer Networks. She is currently pursuing her
Doctorate from Delhi Technological University in the
field of Software Maintenance under the guidance of
Ms Ruchika Malhotra. She has earlier achieved top
rank in her M.Tech (IT) degree and conferred the
University Gold Medal in 2006 from GGS IPU.
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International Journal of Advancements in Research & Technology, Volume 2, Issue 5, M ay-2013
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319
Previously she has acquired her Master’s degree in
Computer Science from Banasthali Vidyapith,
Rajasthan in the year 1993. She's additionally earned
‘PG Diploma in Marketing Management’ from
IGNOU in 1995. She has long teaching experience of
almost 19 years to her credit as faculty and in
administration at various educational institutions in
India. She has also presented number of research
papers in national/international seminars, conferences
and research journals. She can be contacted by E-mail
at a_chug@yahoo.co.in
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Ankita Sawhney is perusing M.Tech(Computer
Science) from University School of Information and
Communication Technology(USCIT),Guru Gobind
Singh Indraprastha University, New Delhi. Her areas
of research interest are Software Engineering, nueral
Networks, software Testing.Previously she has
acquired her Bachelors degree(B.Tech) in Information
Technology from Bharati Vidyapeeth’s college of
Engineering, Guru Gobind Singh Indraprastha
University, New Delhi. She secured 1st position in IT
department of Bharati Vidyapeeth’s College of
Engineering. Apart from this she has been adjudged as
a scholar for both scholastic and non scholastic
subjects in school days and has also achieved
proficiency awards. She can be contacted by E-mail at
ankitasawhney1@gmail.com
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