Optimizing intermediate COCOMO model using BAT algorithm

Software effort estimation is amongst the most important tasks in software project management as many decisions like cost estimation, deadline of submitting of project and timely planning a project are dependent on it. Many models are available for effort estimation and most widely used is COCOMO Model. A lot of research work has been done for the correct and accurate use of effort estimation models. Many techniques have been applied to estimate the coefficient of COCOMO Model. In this paper, a new calibrated Intermediate COCOMO model (for all types of system i.e. organic, semi-detached and embedded) is developed with Bat Algorithm and generated new optimized coefficients. Bat Algorithm is newest Algorithm amongst the category of Meta Heuristic and population based Algorithms. For estimation we have employed Bat Algorithm on NASA 63 dataset and results show that optimized coefficients by Bat Algorithm give better results in terms of MMRE (Mean Magnitude of Relative Error) for all types of projects as compared to coefficients in COCOMO Model which were obtained by Regression Analysis.