Improved Software Cost Estimation Model Using Cost Driver Reduction Based on Water Cycle Algorithm - Wani, Zahid Hussain; Giri, Kaiser Javeed; Bashir, Rumaan (International Journal of Computer Sciences and Engineering, 2019)
Software cost estimation considered to be the critical, is equally vital tasks in software project management. In a highly challenging environment, software project managers are always in a need of robust estimation models inorder to predict the cost of upcoming software development projects accurately. Software cost estimation is the prediction of development effort and calendar time required to develop a software project. It is considered to be the key task as accurate estimation of any software not only accurately estimates development effort, cost, time and growth of a software development project but also yields delivery exactness and correctness vis a viz return an organization in a better schedule of its futuristic software projects. In this paper, software cost estimation is done by proposing a cost driver selection model which is based on an optimization technique called as water cycle algorithm. The proposed cost driver selection model selects only relevant set of cost drivers as an input to estimation process and ignores the very irrelevant cost drivers. In step second, these relevant set of cost drivers originating from step first are assigned to an Artificial Neural Network as its input for the purpose of getting the accurate estimation of software development project cost that needs to be developed. For evaluation purposes, Magnitude of Relative Error, Mean of Magnitude of Relative Error and Median of Magnitude of Relative Error are used as three performance measures to simply weigh the obtained quality of estimation as accuracy. The obtained results were compared with the results of a benchmark study of COCOMO model and another artificial neural network based model. From the comparative result, it becomes evident that the proposed model outperforms the rest of the two existing models.