Artificial Intelligence Model for Software Reusability Prediction System

The most significant invention made in recent years to serve various applications is software. Developing a faultless software system requires the design be resilient. To make more efficient, it essential assess reusability of components used. This paper proposes prediction model named Flexible Random Fit (FRF) based on aging resilience for Service Net (SN) system. developed multilevel optimization technique characteristics such as cohesion, coupling, and complexity. Metrics are obtained from SN system, which then subjected min-max normalization avoid any saturation during learning process. feature extraction process feasible by enriching data quality via outlier detection. classes estimated tool called Soft Audit. Software can predicted effectively proposed FRF-ANN (Flexible - Artificial Neural Network) algorithm. Performance evaluation shows that algorithm outperforms all other techniques, thus ensuring tested using constraint-based testing techniques sure perfect at optimizing making predictions.