Reusability in Software Effort Estimation Model Based on Artificial Neural Network for Predicting Effort in Software Development

Most of the recent research initiatives have focused on development of formal estimation models to improve estimation accuracy. Formal estimation models have been developed to measure lines of code or size of software projects. But, most of the models have failed to improve estimation accuracy. This paper focuses on the reusability in software development effort estimation in early stage of development based on ANN. Software reuse saves the software effort and improves productivity. This paper has proposed a new model called REBEE (Reusability Based Effort Estimation) for better effort estimation accuracy and reliability. This proposed model's accuracy is more improved with the help of ANN Enhanced Back Propagation algorithm. To evaluate the performance of the proposed model a set of projects compared with the existing COCOMO II model by MRE, MMRE and PRED for evaluation of software cost estimation. The final results show that the use of proposed REBEE model effort estimation is reliable, accurate and predictable in the early stage of development of project.