Algorithmic Development of Parametric Optimization And Ranking of Software Reliability Growth Models

Abstract

In today s era of science and technology, software has a great impact on each and every aspect of human being s life. Starting from the simple mobile phones, safety equipment to crucial medical equipment, every device is operated through software. Generally, Software failures have become the most vigorous factor that dismisses the services and effect proper functioning of the system. Therefore, it becomes very important to remove as many probable problems in software as possible. Further, the desire to provide more and more functionality makes the software development process very time-consuming and expensive. As the result, the software developers in IT sector always try to develop software with high reliability. Simply, it can be stated that reliability of the software has become a crucial factor for the software developers in IT sector. newlineTo estimate the reliability of the software, software developers rely on the use of the software reliability growth models. The existing studies reveal the existence of a large number of software reliability growth models (SRGMs) during the past 40 years to estimate software reliability measures such as the number of remaining faults, software failure rate, and software reliability. No single model can be fit for each type of software development environment. Therefore, Selection of an optimal SRGM for use in a particular development case has been an area of interest for researchers and practitioners in the field of software reliability. The present research aims on the evaluation and selection of SRGMs by modelling it as a Multi-criteria decision making (MCDM) problem due the dependency of multiple conflicting attributes. Further, three MCDM approaches namely, Weighted Euclidean Distance Based Approach (WEDBA), Evaluation Based on Distance from Average Solution (EDAS) and Combinative Distance based Assessment (CODAS) have been proposed to solve the present selection problem. Shannon s Entropy approach to calculate the weights of the selection criteria is used first time in suc