Calibrating Intermediate COCOMO Model using Memetic Algorithm

In past few years, numerous analysts and industries related to software given critical consideration on the evaluation of programming effort. The fundamental task in SDLC is software cost estimation. It is also considered as most important part for managing project cost, time, and quality. Numerous researchers provided various cost estimating models, for example, algorithmic models, non-algorithmic models, AI related models, statistical based and nature motivated models. In this work, memetic calculation (MA) is utilized for enhancing the coefficients of intermediate COCOMO model to improve precision for predicting the effort of the project. The procedure is applied on COCOMO NASA programming dataset.