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Reliability Analysis and Modeling of Green Computing Based Software Systems
Abstract
Background
oftware industries are growing very fast to develop new solutions and ease people’s life. Software reliability has been considered as a critical factor in today’s growing digital world. Software reliability models are one of the most generally used mathematical tools for estimation of software reliability. These reliability models can be applied on development of sustainable and green computing-based software’s having their constrained development environments.
Objective
This paper proposes a new reliability estimation model for green IT environment based software systems.
Methods
In this paper, a new failure rate behavior-based model centered on green software development life cycle process has been developed. This model integrates a new modulation factor for incorporating changing needs in each phase of green software development methodology. Parameter estimation for proposed model has been done using hybrid Particle Swarm Optimization and Gravitational Search Algorithm. The proposed model has been tested on real-world datasets.
Results
Experimental results are showing the enhanced capability of proposed model in simulating real green software development environment. Using GC-1 and GC-2 dataset, proposed model is about 60.05% more significant than other models.
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.
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