International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | March-2017
p-ISSN: 2395-0072
www.irjet.net
PARAMETER ESTIMATION OF GOEL-OKUMOTO MODEL BY COMPARING ACO WITH MLE METHOD G.Lavanya1, K.Neeraja2, Sk.Ahamad Basha3 , Dr.Y.Sangeetha4 1G.Lavanya,
Dept. of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Andhra Pradesh, India
2K.Neeraja,
Dept. of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Andhra Pradesh, India
3Sk.Ahamad
Basha, Dept. of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Andhra Pradesh, India
4Dr.Y.Sangeetha,
Dept. of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Andhra Pradesh, India
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Abstract - Statistical Process Control (SPC) is the best
choice to monitor software reliability process. It assists the software development team to identify and actions to be taken during software failure process and hence, assures better software reliability. In this project we propose a control mechanism based on the cumulative observations of failures which is ungrouped data by using an infinite failure mean value function G-O model, which is Non-Homogenous Poisson Process (NHPP) based. By comparing Ant Colony Method (ACO) with Maximum likelihood (MLE) approaches which is used to estimate the unknown parameters of the model and to identify the best approach among these in order to find out the failures at an early stages. Key Words: Statistical Process Control (SPC), Ant Colony Method (ACO), Non Homogenous Poisson Process (NHPP), Maximum likelihood (MLE)
1. INTRODUCTION The monitoring of Software reliability process is a far from simple activity. In recent years several authors have recommended the use of SPC for software process monitoring. The main thrust of the paper is to formalize and present an array of guidelines in a disciplined process with a view to helping the practitioner in putting SPC to correct use during software process monitoring. Software is a program that provides instructions to processor to function, generating the desired result. Software is broadly classified as operating system and application software. The operating system carries out the basic operations of a processor while application software works on a level higher than operating system providing special services. Software plays a key role in the modern life. The life of a software system is considered in a probabilistic way in order to develop a © 2017, IRJET
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Impact Factor value: 5.181
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quality measure for the system called software Reliability. The basic requirement is a specific probability model from statistical science to be modeled for software failure data in order to predict the future failure time of the system. In this project the software failure pattern is approximated by a Ant Colony Optimization method and maximum likelihood estimation method. The failure process is mathematically evaluated and empirically examined for live software failure data with the theory of mathematical statistics. Software reliability modelling and prediction during product development is an area of reliability that is getting more focus from software developers. The use of software reliability growth models plays an important role in measuring improvements, achieving effective and efficient test scheduling during the course of a software development project, determining when to release a product or estimating the number of service releases required to reach a reliability goal. Reliability is a real -world phenomenon with many associated real-time problems. One of the common practices in manufacturing industry is Statistical Process Control (SPC). The investigation on quantitative mechanisms as an aid to control process variation led to the application of the SPC since 1930’s. The idea of applying SPC to software development however, is exemplified mainly by Capability Maturity Model (CMM) in mid 90’s. Although its benefits are accepted for manufacturing companies, there have been many debates about its application in software development.
1.1 Software Reliability Software Reliability is an important quality characteristic of a software which can evaluate and predict the operational quality of software system during its development. Software Reliability is the probability of failure free operation of software in a specified environment for a specified period of time Statistical Process Control (SPC) concepts and methods are used for improving the software reliability by identifying ISO 9001:2008 Certified Journal
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