Comparative Analysis of Software Components Reusability Level using GFS and ANFIS Soft-Computing Techniques

The quest to develop software of great quality with timely delivery and tested components gave birth to reuse. Component reusability entails the use (re-use) of existing artefacts to improve the quality and functionalities of software. Many approaches have been used by different researchers and applied to different metrics to assess software component reusability level. In addition to the common quality factors used by many authors, such as customisability, interface complexity, portability and understandability, this study introduces and justifies stability, in the context of volatility, as a factor that determines the reusability of software components. Sixty-nine software components were collected from third party software vendors, and data extracted from their features were used to compute the metric values of the five (5) selected quality factors. Genetic-Fuzzy System (GFS) was used to predict the level of the components' reusability. The performance of the GFS was compared with that of Adaptive Neuro-Fuzzy Inference System (ANFIS) approach using their corresponding average RMSE (Root Mean Square Error), in order to ascertain the level of accuracy of the prediction. The results of the findings showed that, GFS with an RMSE of 0.0019 provides better reusability prediction accuracy compare to ANFIS with an RMSE of 0.1480.