Investigating the Effect of Software Project Type on Accuracy of Software Development Effort Estimation in COCOMO Model

Software development effort is one of the most important metrics in field of software engineering. Since accurate estimating of this metric affects the project manager plans, numerous research works have been performed to increase the accuracy of estimations in this field. Almost all the previous publications in this area used several project features as independent features and considered the development effort as dependent one. Constructive Cost Model (COCOMO) is the most famous algorithmic model for estimating the software development effort. Despite the fact that many researchers have tried to improve the performance of COCOMO using non-algorithmic methods, all of which have estimated the development effort regardless of the project type. In this paper, the effect of considering the project type in estimating was investigated by means of neural networks. The obtained results were compared with the original COCOMO and neural network. The comparisons showed that the software project type can affect the accuracy of estimations significantly.