Fuzzy Logic Control Neural Network Hybrid System for Identification, Classification of Software Reusability Components through Relationships of Lattice Factors

The software reusability mode is highly required field for successful execution of artificial intelligence, machine learning based applications to fulfill the present and future human needs. The identification, classification and measuring the required components are key-roles concerns in fast development of software reusability components for producing the high quality software. This paper is proposing the Fuzzy Logic Controller Neural Network Hybrid System which is implicated to recognize the affecting factors of component reusability execution by instituting the strong, week relationships in among these considered factors to fulfill the user requirement. This approach considered eleven effecting factors such as Portability, Reliability, Complexity, Efficiency, Quality, Security, Cost, Maintainability, Cohesion, Availability and Flexibility along with their related attribute metrics. This paper has composed with four major objectives such as: the comparative analysis of Fuzzy Logic Control System and Neural Networks with their advantages and execution flow; The implications of Fuzzy Logic Control Neural Network Hybrid System architecture design for concern problem; The proposed FLCNNHS based algorithm and execution data flow diagram for executing the considered software reusability effecting factors along with their supporting attributes metrics for identification and Classification of Reusability Components through Strong, Week Relationships of Lattice Factors which is implacable for designing the better quality software product; and described the experimental analysis and results through proposed algorithmic approach. This innovative approach is more helpful for software developers to choose highly accurate components which are more required to build the high efficiency secure systems.