Open AccessEstimate reliability of component-based software sys-tem using modified neuro fuzzy model
Author(s) -
Ravi Kumar Sharma,
Parul Gandhi
Publication year - 2017
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v6i2.7722
Subject(s) - component (thermodynamics) , computer science , reliability (semiconductor) , reliability engineering , software quality , fuzzy logic , data mining , software , inference , dependency (uml) , artificial intelligence , software development , engineering , programming language , quantum mechanics , thermodynamics , power (physics) , physics
There are many algorithms and techniques for estimating the reliability of Component Based Software Systems (CBSSs). Accurate esti-mation depends on two factors: component reliability and glue code reliability. Still much more research is expected to estimate reliability in a better way. A number of soft computing approaches for estimating CBSS reliability has been proposed. These techniques learnt from the past and capture existing patterns in data. In this paper, we proposed new model for estimating CBSS reliability known as Modified Neuro Fuzzy Inference System (MNFIS). This model is based on four factors Reusability, Operational, Component dependency, Fault Density. We analyze the proposed model for diffent data sets and also compare its performance with that of plain Fuzzy Inference System. Our experimental results show that, the proposed model gives better reliability as compare to FIS.