an enhanced adaptive pso based neural network (apso … · the product, software dependability is...

ELK ASIA PACIFIC JOURNAL OF COMPUTER SCIENCE AND INFORMATION SYSTEM

ISSN 2349-9392 (Online); DOI: 10.16962/EAPJMRM/ISSN 2454-3047/2015; Volume 5 Issue 2 (2019)

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AN ENHANCED ADAPTIVE PSO BASED NEURAL NETWORK (APSO-NN) FOR SOFTWARE

RELIABILITY PREDICTION

J Ramesh Reddy

[email protected]

ABSTRACT

In recent years, the advancement in the software field has been enormous and hence software with better

reliability is the need for the day. Demand on high reliable software requirement has seen the emergence

of number of techniques for predicting the software reliability. Quality of a software can be determined by

predicting how reliable the software will be for its customers. Software reliability prediction techniques have

been implemented in recent times based on testcase optimization. The main objective of our proposed method

is to develop an enhanced prediction technique wherein we employ adaptive functionality enabled PSO which

can be used for optimizing the error functionality of neural network classifier. This optimal selection of error

functionality with respect to the test cases can enhance the prediction results. Based on the training and testing

data we shall predict the performance software where in accuracy, execution time and fitness functionality are

measured. The proposed APSO-NN is then compared with existing algorithms to show the effectiveness of our

method. From the results estimated, we achieved prediction accuracy of 95% in comparison with existing

approaches which has maximum accuracy of 87.5%.

Keywords: Software reliability, prediction, fitness function, adaptive PSO, Neural network

Introduction

The present-day society is exceptionally

engaged in software roles. The

organizations developing software along

with the software engineers take up the

responsibility of maintaining the reliability,

quality and satisfaction of the customers

with the products of software. Among the

quality control approaches, software testing

is mostly considered to be a significant one

[1]. The objective of software engineering

is the development of tools and methods

required in developing applications that are

increasingly steady and viable. So as to

evaluate and enhance the application

quality during the process of development,

the managers and developers utilize various

measurements [2]. Due to various technical

motives like source code access, enhanced

quality of the product, lesser cost of

development, shorter cycles of

development increasing number of

developers and organizations are

developing their products based open

source segments [3].

To evaluate the failure conduct of the

software framework, its reliability is a

significant parameter to assist engineers by

arranging sufficient test exercises. By

encouraging the earlier estimation of the

dependability, engineers are able to

reallocate the resources of testing

dynamically and decrease the cost for bug

fixing once the software is released [4]. As

the applications of the computer pervade

our day to day life, reliability turns into a

significant attribute for the computer

frameworks. The reliability of the software

is hence one of the most significant

highlights for a crucial software

framework. The probability of operating

the software free of failures for a



10

predetermined time frame under a

predefined domain is defined as software

reliability by ANSI. Practically speaking, it

is extremely challenging to measure the

quality and reliability of the software by the

project managers [5].

So as to compute and foresee the quality of

the product, software dependability is

determined to be an important trait [6].

Reliability can be characterized as a

likelihood of zero-failure activity of

specific software at a particular moment of

time in a particular condition [7]. So as to

guarantee the total unwavering software

reliability, it is critical accurate modelling

of the product dependability and to foresee

the plausible patterns. Certain, but

significant measurements, for example,

timeframe, MTBF, fault number and

MTTFs through SRGMS would be useful

for such conditions [8].

The neural network incorporates an

essential lead over diagnostic models since

they require just a history of failure as input,

no hypotheses. Utilizing that input the

model of the neural network consequently

builds up its own internal failure model and

estimates future failures. Additionally,

Cuckoo search helps in assessing the values

of the weight and that be used in a neural

system for the prediction of software

reliability.

The effort on the prediction of software

development with high accuracy is yet to a

great extent an unsolved issue. Hence, there

is a progressing level of activities in this

field. An enormous number of various

forecast models have been proposed in the

course of recent years. Till now, the

absence of converging studies on models of

software prediction is ineffectively

comprehended, and it has been a riddle to

the community exploring the models of

software prediction for a long time.

Unmistakably, we have to combine the

information on forecast models and

research processes; we have to comprehend

why we have acquired such contradictory

conclusions on this issue [9]. Having the

option to foresee the fault number dwells in

the software helping considerably in

determining/calculating the release day of

the software and oversee the resources of

artificial neural networks (ANN) with the

prediction of software reliability attracted

more interest in the research [10]. In view

of each of these contemplations, we have

proposed a modified ANN classifier based

on the cuckoo search for predicting the

software reliability since it, to a great

extent, improves the accuracy of

classification.

Related work:

Various researches have been proposed by

researchers for the prediction of Software

reliability. Below mentioned are some

studies applied for assessing the state-of-art

work on the reliability prediction models.

Prediction of software reliability was

significant for reducing the expenses and

enhancing the viability of the process of

developing the software. As a significant

technique, relative information during the

lifecycle of the program was utilized to

examine and foresee software reliability.

But, foreseeing the changeability of

reliability with time was exceptionally

troublesome. Support vector regressions

(SVR) have been broadly applied for

solving non-linear prediction issues in

numerous fields, for example, predicting

software reliability and have got good

performance in different situations, but was

still challenging to choose its parameters.



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Optimization algorithms like genetic

algorithms (GA), are generally utilized for

discovering better parameters of SVR, but,

existing strategies for choosing parameters

required typically have a few drawbacks.

Jin [11] introduced a method to resolve the

shortcomings of GA, for example, the local

minima and the convergence issues,

simulated annealing (SA) and GA are

combined to form an optimization

algorithm, referred to as GA-SA, it is then

applied to SVR for prediction of software

reliability. The developed GA-SA-SVR

model was then contrasted with other

models of software reliability through real

information of software failures. The

experimental outcomes demonstrated that

the proposed GA-SA-SVR model could

acquire better forecast results over different

models and had a relatively accurate

capability of prediction.

Albeit numerous algorithms and methods

have been created for evaluating the

reliability of component-based software

systems (CBSSs), considerably more

research was required. Precise estimation of

the CBSS reliability was troublesome in

light of the fact that it relies upon two

elements: reliability of component and glue

code. The strategies of soft computing

could help in solving the issues wherein the

solutions are either unpredictable or

uncertain. Various soft computing

techniques for assessing CBSS reliability

have been developed. These procedures

gain knowledge from past data along with

capturing the existing data patterns. The

two fundamental components of soft

computing are fuzzy logic and neural

networks. Tyagi and Sharma [12]

developed a model for evaluating the

reliability of CBSS, known as a versatile

neuro-fuzzy inference system (ANFIS),

that depended on these two fundamental

components of soft computing, and

compared the performance to that of a plain

FIS (fuzzy inference system) for various

sets of data.

Singh et al. [13] have proposed an approach

to address transition probability of utilizing

the markov model between two stages.

Predicted reliability has been estimated and

compared with computed reliability to

show the model effectiveness. Cong Jina

and Shu-Wei Jin [14] proposed an approach

to predict software reliability using IEDA

SVR model which employees optimization

of SVR model. Proposed approach was the

compared with existing algorithms to show

the enhancement made by proposed

techniques.

Park and Baik [15] have proposed a

multiple reliability model selection

approach were it operates dynamically to

predict the reliability. Pattern of multi-

criteria derived from multi reliability

models were utilized with reduced error

pruning decision trees. Roy [16] have

proposed a multi-layer feedforward

artificial neural network (ANN) software

reliability estimation and prediction using

logistic growth curve model. Variants of

activation function are derived by the

authors for improving the accuracy finally

comparing woth different training

algorithms to show their class outperforms

the existing techniques.

Based on the review conducted, it is

inferred that there is a needs for developing

a prediction model that can with stand

automatic testcase selection and there by

utilizing the selected testcase for the

reliability prediction. Execution time and

fitness values are very complex in most of

the recent researches reviewed and hence



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solutions for surpassing these defects are

the need for the day which motivated us to

developed the proposed research utilizing

adaptive nature of PSO and neural network

Classifier.

Proposed reliability prediction technique

for software testing:

The probability of developing a fault free

software that can withstand its functionality

over a longer time period is termed as

software reliability. As the need for

software has increased over time, the

requirement for reliability prediction has

increased rapidly. Predicting software

reliability in turn provides measures to

improve the software lifecycle. Early

reliability prediction of any software can in

turn assist in formulating strategies to build

the software with consideration to cost

overrun, reliability improvement, software

failures. For prediction of software

reliability, there has been a lot of research

carried out by many with the aim of

improving the quality and lifetime of a

software. Many approaches follow

automatic test case generation and

prioritization which in turn aids in

executing the quality of the software. Here,

we have employed an enhancement in

software test case selection with integration

to artificial intelligence. The process we

employed here is to make an extensive

selection of test cases through optimal

classifier that can improve the prediction

accuracy and software quality.

3.1 Steps involved in our proposed

method:

For reliability prediction, different software

parameters will be considered inorder to

train and predict the reliability in ML

approach. The figure 1 given below shows

the work flow which is being carried out in

our proposed research. As per the flow

diagram mentioned, initially we select an

input software for which we have to predict

the software reliability. The same is

calculated based on the components and

testcases being generated. The optimal

selection of testcases further provides better

reliability prediction. We have proposed a

novel approach where enhancement in

classifier algorithm is made by integrating

adaptive particle swarm optimization with

neural network. Here the integration is done

inorder to enhance the calculation of the

error values used for training and testing the

classifier with respect to the input

parameters. By implementing this concept

of error calculation, the predictive accuracy

can been highly improved. Since the work

deals with testcase selection, the generated

initial testcases will be subjected to this

transformation while calculating error

factor and hence we can obtain a better

prediction accuracy. Each steps in the

proposed methodology are explained in

detail below,

3.1.1 Test case generation

Testcases are usually estimated or

generated inorder to test the feasible

combinations of any software applications.

Testcase generation can assist the software

developers in finding out or exploring the

failures/defects in the software at the earlier

instances. The testcases are generated based

on the specific software functionality.

Triangle program is employed here for

which various test cased are estimated and

calculated the priority. The generated

testcases will be now trained using the

proposed enhanced classifier which can

further prediction the reliability when a test

data is inserted on the same. For instance, If



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there are N testcases say NTTT ,....,, 21 ,

each of this N test cases will be trained and

tested in the proposed predictor hence to

obtain enhanced accuracy.

3.1.2 Reliability Prediction using Adaptive

functionality integrated PSO –NN

classifier:

As described in the previous sessions, the

major contribution of our proposed study is

implementation of enhanced prediction

algorithm that involves the integration of

optimization algorithm and the classifier

together forming a hybrid classification

technique. The input testcases are applied to

the enhanced classifier which is an adaptive

form of neural network. We have employed

Feed Forward Back Propagation Neural

Network for classification. As general NN,

there are three layers like input, hidden and

output layer where the weight

calculation/error value calculation will be

carried out between the stages of Input and

hidden and the same will be integrated with

hidden to output layer. As shown in the

below figure, the input test cases are fed to

the neural network in the input layer say

NTTT ,....,, 21 . The input testcases are then

fed to the hidden layer where the training of

the input features are carried out. Here the

inputs along with feed forward error

functionality are integrated and the same is

estimated as the trained data. In normal

network functionality, the process is carried

out in random manner. Here we have

employed and enhanced approach were the

error functionality will be selected using

optimal criteria. We employ adaptive

particle swarm optimization here.

=

The complete functionality of neural

network is enhanced with this integration of

Adaptive PSO based training phenomena.

The objective function of the adaptive PSO

is integrated here with the error learning to

employ enhancement to the prediction

results. The complete working strategy is

shown below,

Steps involved in APSO-NN Prediction

algorithm:

1) Initiate the weights for every neuron

undertaken in the proposed Neural network

model. While initiation of weights, input

layers are excluded.

2) Feed the neural network with input

features parameters of testcases

NTTT ,....,, 21 , hidden functionality as

1HN , 2HN , …., NHN and output as

NOOO ,....,, 21

3) Activation function for the output layer

is then estimated. The expression for

activation function is calculated as below,

eAt fn

1

1)(

4) Once the activation function is defined,

the next stage is the error functionality

learning wherein we have modified the

approach by integrating objective function

of APSO. The process is given as below,

Initiate the population size sP which

begins from NTTT ,....,, 21

Derive the fitness function for the

adaptive pso with consideration to the input

values and is termed as ,

WrateAp IELFit

where,



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rateEL - learning rate of ANN

WI - Inertial weight of APSO

5) Once the fitness function is derived, then

pbest and gbest are initialized. Velocity id

the n computed along with the position

function.

6) Once the values are calculated,

depending on the fitness criteria, the

updation will be carried out.

7) Finally, the optimized results is obtained

at the output layer of neural network with

better prediction accuracy.

Results and discussion:

Software reliability prediction we proposed

here is based on hybridization concept

where we integrate adaptive pso

optimization technique with neural network

forming an enhanced classifier.

Implementation is carried out using JAVA

using netbeans IDE. Basis of the proposed

scheme, we have designed an enhanced

prediction technique where the optimal

selection of error factor is calculated in the

neural network that has aided in enhanced

results prediction. Here we measure our

proposed algorithm in terms of

classification accuracy, execution time and

the fitness functionality obtained by

incorporating the algorithms.

The table 1 given below shows the fitness

value of our proposed method with APSO

method using different iterations. For the

same iteration, the fitness value is estimated

with existing GA as well and the values are

tabulated. The fig 3 given below shows the

comparison of the fitness value. The graph

shows that our proposed method has

delivered better fitness value which aids in

improving the reliability of the software.

The fig 3 given below shows the

comparison of the fitness value with our

proposed method and compared with the

existing GA algorithm. As inferred from the

graph, the proposed approach of APSO has

outperformed GA in terms of fitness values.

For iteration 5, the proposed method has a

better fitness value of 15.2 when compared

with that of existing GA algorithm which is

around 10.35. As the iteration increases the

fitness value reduces and for iteration 20 the

proposed approach has fitness of 10 when

compared with a low fitness of 7.9 obtained

using GA.

For proving the effectiveness of the

proposed approach, we estimated the

execution time under different iterations

using the algorithm we proposed and

compared the same with existing

algorithms. The findings are tabulated as

below,

Based on the observed values above, the

graphical representation for the proposed

and existing techniques are given in figure

4 below. As inferred from the below, graph,

the proposed method of APSO-NN has

better execution time which is 88.7 ms for

25 iterations when compared with the

existing algorithm like SVM and ANN

which has 137.7 ms and 215.7 ms

respectively.

The performance of the proposed method is

compared using the classification accuracy

with that of some existing methods [17].

The table 3 given below shows the accuracy

values for proposed and existing method.

Based on the evaluated results tabulated

above, comparative graph is shown in fig 5.

From the comparative graph it is inferred

that the proposed technique with APSO and

ANN has outperformed the existing

prediction techniques. The classification



15

accuracy obtained using our proposed

algorithm is 96.5 % which is highly an

enhanced result when comparing with

existing algorithms like SVM and ANN

which has classification accuracy of 74.6%

and 80.7% respectively.

Conclusion and future scope:

In this research work, we carried out an

advanced approach for software reliability

prediction by utilizing Adaptive PSO

integrated with neural network. Major

motivation for this research is to overcome

the issues concerned with prediction

accuracy and selection of respective

testcases. As a results of our review and

research, we developed a neural classifier

which has the adaptive functionality of PSO

which makes it more effective in terms of

selecting the testcases and further

processing it in predicting the accurate

reliability. From the results, it is evident

that our proposed prediction model utilizing

APSO and NN has outperformed existing

model in terms of prediction accuracy

which reads 96.5 % when comparing with

existing algorithms like SVM and ANN

which has classification accuracy of 74.6%

and 80.7% respectively. In future we have

planned to utilizing more hybridized

algorithms and evaluate their performance

in a manner that the prediction rate can be

further improved guiding the software

developers to design quality rich

software’s.

Reference:

[1]. Carina Andersson, "A replicated

empirical study of a selection method for

software reliability growth models,"

Journal of Empirical Software Engineering,

Vol. 12, No. 2, pp. 161–182, Apr 2007.

[2]. Jehad Al Dallal ,"Mathematical

Validation of Object-Oriented Class

Cohesion Metrics", International Journal

Of Computers , Vol. 4,No.2, 2010.

[3]. Heikki Orsila, Jaco Geldenhuys, Anna

Ruokonen and Imed Hammouda," Update

Propagation Practices in Highly Reusable

Open Source Components",In.proc.of 20th

World Computer Congress on Open Source

Software,Milano, Italy,Vol. 275 ,pp.159-

170, Sep 7-10, 2008.

[4]. Chao-Jung Hsu and Chin-Yu Huang,

"An Adaptive Reliability Analysis Using

Path Testing for Complex Component-

Based Software Systems", IEEE

Transactions On Reliability, Vol. 60, No.

1,2011.

[5]. Chin-Yu Huang and Michael R. Lyu,

"Optimal Release Time for Software

Systems Considering Cost, Testing-Effort,

and Test Efficiency", IEEE Transactions

On Reliability, Vol.54, No.4, 2005.

[6]. Khurshid Ahmad Mir, "A Software

Reliability Growth Model," Journal of

Modern Mathematics and Statistics, Vol. 5,

No. 1, pp. 13-16, 2011.

[7]. Chin-Yu Huang, Sy-Yen Kuo and

Michael R. Lyu, "An Assessment of

Testing-Effort Dependent Software

Reliability Growth Models," IEEE

Transactions on Reliability, Vol. 56, No. 2,

pp. 198-211, Jun 2007.

[8]. S. M. K. Quadri, N. Ahmad and Sheikh

Umar Farooq, "Software Reliability

Growth modeling with Generalized

Exponential testing –effort and optimal

Software Release policy," Global Journal of

Computer Science and Technology, Vol.

11, No. 2, pp. 27-42, Feb 2011.

[9]. Ingunn Myrtveit, Erik Stensrud and

Martin Shepperd, "Reliability and Validity

in Comparative Studies of Software

Prediction Models", IEEE Transactions On

Software Engineering, Vol.31, No.5, 2005.

[10]. Rita G. Al gargoor and Nada N.

Saleem, "Software Reliability Prediction



16

Using Artificial Techniques",IJCSI

International Journal of Computer Science

Issues, Vol. 10, Issue 4, No 2,2013.

[11]. C. Jin, "Software reliability

prediction based on support vector

regression using a hybrid genetic algorithm

and simulated annealing algorithm", IET

Software, Vol.5, No.4, pp. 398–405, 2011.

[12]. Kirti Tyagi and Arun Sharma, "An

adaptive neuro fuzzy model for estimating

the reliability of component-based software

systems", Applied Computing and

Informatics, Vol.10, No.2, pp.38–51, 2014.

[13]. Lalit K. Singh, Gopika Vinod and

Anil K. Tripathi, "Approach for parameter

estimation in Markov model of software

reliability for early prediction: a case

study", IET Software, Vol. 9, No.3, pp.65–

75,2015.

[14]. Cong Jina and Shu-Wei Jin,

"Software reliability prediction model

based on support vector regression with

improved estimation of distribution

algorithms”, Applied Soft Computing,

Vol.15, pp.113–120, 2014.

[15]. Jinhee Park and Jongmoon Baik,

"Improving software reliability prediction

through multi-criteria based dynamic

model selection and combination", Journal

of Systems and Software, Vol.101, pp.236-

244, 2015.

[16]. Pratik Roy, G.S. Mahapatra and

K.N. Dey, "Neuro-genetic approach on

logistic model based software reliability

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List of Figures and Tables

Any open source

input software

for reliability

check

Generating test

cases

Adaptive

particle Swarm

Optimization

Neural

network

classifier

APSO-NN Classifier for prediction

Reliability prediction

Condition:

Sum>threshold



17

Fig 1: Proposed process flow

Figure 2: Structure of NN classifier for the proposed work.

Iterations

Fitness value

Using APSO

Using GA

5 15. 2 10.35

10 14.2 9.89

15 12.18 9.21

20 10.34 8.13

25 10 7.9

Table.1 Fitness value for different iteration.



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Fig 3: Graphical representation for fitness value obtained using APSO and GA optimization

algorithms.

Iterations Execution time (ms)

Using APSO-NN ANN SVM

5 39.7 89 120

10 46.3 94.5 132

15 57.7 105.9 155.8

20 75.5 123 170

25 88.7 137.7 215.7

Table.2 Execution time for different iteration

0

2

4

6

8

10

12

14

16

18

5 10 15 20

Fitness value

Using APSO

Using GA



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Fig 4: Graphical representation for Execution time at different iteration using proposed and

exiting techniques

Methods

Classification Accuracy (%)

SVM

74.6

ANN

80.7

APSO-NN

96.5

Table 3: Comparison of prediction accuracy measures for our proposed and existing method

0

20

40

60

80

100

120

140

160

180

5 10 15 20

Execution time under different iterations

APSO-NNANNSVM



20

Fig 5: Graphical representation of prediction accuracy measures for our proposed and

existing method

0

20

40

60

80

100

120

SVM ANN APSO-NN



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