Reliability prediction analysis of aspect-oriented application using soft computing techniques

Accurate estimation is the foremost goal of any forecasting model. Software reliability is one of the leading research issues of the software organization. Recently, various applications of soft computing technology have attempted. Software reliability is one of the quantitative indicators of software quality. The Software Reliability Growth Model (SRGM) is used to evaluate the reliability obtained in different stages of testing. The reliability of the software depends on several factors (extensibility, sustainability, design stability, and configurability). Moreover, these factors are related to each other and directly or indirectly affect the software development process. Among various soft computing technologies, models based on artificial neural networks are well known, which extremely needs more research work and endeavors to discover the most reasonable model for software quality. The purpose of this paper is to examine the reliability of the software using soft computing techniques, which is the most efficiently used tool to evaluate its predictive power. It provides a new comparative analysis to find the most suitable and accurate artificial neural network based on the software reliability model. To answer this question, we need to evaluate the model. We use MMRE, SD, RSD, and PRED (N) to analyze the performance of the competitive model. The applicability of a particular soft computing technique is an open question because it depends mostly on the nature and characteristics of the problem. Every software project has its development behavior and complexity model. Therefore, more research is needed to predict and analysis of the results. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advances in Materials Research-2019.