New Failure Rate Model for Iterative Software Development Life Cycle Process
📝 Abstract
Software reliability models are one of the most generally used mathematical tool for estimation of reliability, failure rate and number of remaining faults in the software. Existing software reliability models are designed to follow waterfall software development life cycle process. These existing models do not take advantage of iterative software development process. In this paper, a new failure rate model centered on iterative software development life cycle process has been developed. It aims to integrate a new modulation factor for incorporating varying needs in each phase of iterative software development process. It comprises imperfect debugging with the possibility of fault introduction and removal of multiple faults in an interval as iterative development of the software proceeds. The proposed model has been validated on twelve iterations of Eclipse software failure dataset and nine iterations of Java Development toolkit (JDT) software failure dataset. Parameter estimation for the proposed model has been done by hybrid Particle Swarm Optimization and Gravitational Search Algorithm. Experimental results in-terms of goodness-of-fit shows that proposed model has outperformed Jelinski Moranda, Shick Wolverton, Goel Okummotto Imperfect debugging, GS Mahapatra, Modified Shick Wolverton in 83.33 % of iterations for eclipse dataset and 77.77% of iterations for JDT dataset.
💡 Analysis
Software reliability models are one of the most generally used mathematical tool for estimation of reliability, failure rate and number of remaining faults in the software. Existing software reliability models are designed to follow waterfall software development life cycle process. These existing models do not take advantage of iterative software development process. In this paper, a new failure rate model centered on iterative software development life cycle process has been developed. It aims to integrate a new modulation factor for incorporating varying needs in each phase of iterative software development process. It comprises imperfect debugging with the possibility of fault introduction and removal of multiple faults in an interval as iterative development of the software proceeds. The proposed model has been validated on twelve iterations of Eclipse software failure dataset and nine iterations of Java Development toolkit (JDT) software failure dataset. Parameter estimation for the proposed model has been done by hybrid Particle Swarm Optimization and Gravitational Search Algorithm. Experimental results in-terms of goodness-of-fit shows that proposed model has outperformed Jelinski Moranda, Shick Wolverton, Goel Okummotto Imperfect debugging, GS Mahapatra, Modified Shick Wolverton in 83.33 % of iterations for eclipse dataset and 77.77% of iterations for JDT dataset.
📄 Content
REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 Abstract Software reliability models are one of the most generally used mathematical tool for estimation of reliability, failure rate and number of remaining faults in the software. Existing software reliability models are designed to follow waterfall software development life cycle process. These existing models do not take advantage of iterative software development process. In this paper, a new failure rate model centered on iterative software development life cycle process has been developed. It aims to integrate a new modulation factor for incorporating varying needs in each phase of iterative software development process. It comprises imperfect debugging with the possibility of fault introduction and removal of multiple faults in an interval as iterative development of the software proceeds. The proposed model has been validated on twelve iterations of Eclipse software failure dataset and nine iterations of Java Development toolkit (JDT) software failure dataset. Parameter estimation for the proposed model has been done by hybrid Particle Swarm Optimization and Gravitational Search Algorithm.
Experimental results in-terms of goodness-of-fit shows that proposed model has outperformed Jelinski Moranda, Shick Wolverton, Goel Okummotto Imperfect debugging, GS Mahapatra, Modified Shick Wolverton in 83.33 % of iterations for eclipse dataset and 77.77% of iterations for JDT dataset.
Index Terms Software development life cycle, Iterative software development life cycle, Optimization, Nature-inspired algorithms, Software reliability models.
ACRONYMS
SDLC Software Development Life Cycle
LLF Log Likelihood Function
MLE Maximum Likelihood Estimation
SSE Sum of Squared Error
MSE Mean Square Error
JM Jelinski Moranda
PSO-GSA Particle Swarm Optimization and
Gravitational Search Algorithm
Sangeeta is with Delhi Technological University, New Delhi, India
(e-mail: sangeeta@dtu.ac.in)
K. Sharma is with Delhi Technological University, New Delhi, India
(kapil@ieee.org)
Manju Bala is with Indraprastha College for Women, Delhi University,
New Delhi, India (manjugpm@gmail.com )
NOTATIONS ( ) it
Failure Intensity
i
f(t ) Probability Density Function
i
F(t ) Cumulative Distribution Function
i
R(t )
Reliability Function
L(N) Likelihood Function
Modulation factor for representing changing
needs in an iteration of software development
Modulation parameter that represents newly
added functionality and user acceptance in an
iteration
N Number of initial faults in iteration
p
Probability of fault removal in iteration
r Probability of fault introduction in iteration
-1
in
Cumulative number of failures at ( -1)th
i
failure
interval
I. INTRODUCTION
ITH the growing advances in the digital world, software
development demand from industries is growing at an
exponential rate. Due to enormous demand and lack of
time and budget, software companies are not able to develop
fault-free software. Latest tools and techniques have been
applied for the development of defect-free software, but still, it
is not possible for software developers to develop defect-free
software practically. Software must go through exhaustive
testing and debugging, which requires time and money to
enhance the reliability [1] [2]. The occurrence of fault is
inevitable in the current demand of software. There should
have some means to avoid software failures so that devastating
losses whether related to life or any other field could be
evaded.
According to IEEE standard 729 [3], reliability is the most
significant quality aspect of the software. If we could measure
the reliability of software under development, better we can
predict whether the software would be operational in the future
or not. Reliability estimation process must be precise to
provide information to the manager like what should be the
release time of the software and amount of man-hour
consumption etc. while developing any software [4]. Software
reliability models are one of the ways to simulate software
reliability estimation curve to predict the reliability of the
system under study. Numerous reliability estimation models
for software have been developed, and all are working on
specific applications, specific environments, datasets and
New Failure Rate Model for Iterative Software
Development Life Cycle Process
Sangeeta, Member, IEEE, Kapil Sharma, Member, IEEE, and Manju Bala
W
REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 2 assumptions made by them. Then, what is the need for developing new software reliability models? First need lies in the fact that, among the available research in software reliability model development [5] [6] [7] [8] [9] [10], all deve
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