Component-Based Software System using Computational Intelligence Technique for Reliability Prediction

Abstract

Software developers and companies are working to create reliable software for their users. This has resulted in the need for enhancing predictive software quality models for risk-less software development based on the type of software. Development and testing costs can be reduced by reusing existing codes and components. Re-use of components results in increased productivity, quality, and maintainability of the software as iterations are made for incremental changes, new developments altogether, and reducing development cost, time, effort. Component-based software systems along with soft computing techniques should be used for having more reliable software. With the increase in demand for software quality prediction, several techniques have been used to predict software quality. The focus of this paper is on the software product's quality; metrics and we thoroughly review the literature of existing computational intelligence/soft computing techniques and analyze the findings according to the techniques. We have discussed models for software quality prediction for component-based software and computational intelligence techniques which are widely prevalent for software quality prediction and compare different techniques. In our study, we have done a comparative analysis of soft computing techniques. This study will help the other researchers to study and understand which technique would be more helpful in making the software reliability prediction model for component-based software by combining different computational techniques.