Estimation of Software Reusability for Component Based System Using Soft Computing Techniques

Soft computing techniques play very important role in developing software engineering applications. These consist of fuzzy logic system, neural network model and genetic algorithm techniques. Among these fuzzy logic and neural network techniques are broadly used to assess software reusability, software maintainability, software understandability etc. Software reuse is defined as software development with several existing modules. This paper presents a model based on different factors namely Modularity (MD), Interface Complexity (IC), Maintainability (MN), Flexibility (FX) and Adaptability (AD) for the assessment of software reusability using soft computing techniques via fuzzy logic and neural network. This is done by assuming different membership functions such as Triangular (trimf), Trapezoidal (trapmf) and Gaussian (guassmf) membership functions defined in MATLAB for these parameters in order to predict the reusability values. Then these data sets are applied to our proposed Neural Network Model. Our work compares the sensitivity analysis of the two models and shows which one is better. Our approach is depending on these software metrics for the identification and evaluation of reusable components. Software reusability is likely to have a bright future and a remarkable work for research. This effort will help developers and researchers to choose the finest component related to the reusability, which would help in improving the performance and efficiency of the whole software system.