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Estimating Software Reliability Using Ant Colony Optimization Technique with Salesman Problem for Software Process

Journal: International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE) (Vol.7, No. 2)

Publication Date:

Authors : ; ;

Page : 20-29

Keywords : Software Reliability; Bio-inspired Computing; Ant Colony Optimization (ACO) technique; Travelling Salesman Problem (TSP).;

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Software reliability means it is a failure free operation of software for a specific period of time under specified environment. Software reliability is defined as the probability with which the software will operate without any failure for a specific period of time in a specified environment. It is one the important software quality features. Software reliability, when estimated in early phases of software development life cycle, saves lot of money and time as it prevents spending huge amount of money on fixing of defects in the software after it has been deployed to the client. Software reliability prediction is very challenging in starting phases of life cycle model. Software reliability estimation has thus become an important research area as every organization aims to produce reliable software, with good quality and error or defect free software. There are many software reliability growth models that are used to assess or predict the reliability of the software. These models help in developing robust and fault tolerant systems. In the past few years many software reliability models have been proposed for assessing reliability of software but developing accurate reliability prediction models is difficult due to the recurrent or frequent changes in data in the domain of software engineering. As a result, the software reliability prediction models built on one dataset show a significant decrease in their accuracy when they are used with new data. The objective of this paper is to introduce a new approach that optimizes the accuracy of software reliability predictive models when used with raw data. Ant Colony Optimization Technique (ACOT) is proposed to predict software reliability based on data collected from literature. An ant colony system by combining with Travelling Sales Problem (TSP) algorithm has been used, which has been changed by implementing different algorithms and extra functionality, in an attempt to achieve better software reliability results with new data for software process. The intellectual behavior of the ant colony framework by means of a colony of cooperating artificial ants are resulting in very promising results. The method is validated with real dataset using Mean Time to Failure (MTTF) and Mean Time Between Failure (MTBF).

Last modified: 2018-05-10 16:42:28