A Hybrid of Ant Colony Optimization and Chaos Optimization Algorithms Approach for Software Cost Estimation

The main challenge in the production and development of large and complex software projects is the cost estimation with high precision. Thus it can be said that estimating the cost of software projects play an important role in the organization productivity. With the increasing size and complexity of software projects the demand to offer new techniques to accomplish this important task increases day by day. Therefore, researchers have long attempted to provide models to fulfill this important task. The most documented algorithmic model is the Constructive Cost Model (COCOMO), which was introduced in 1981 by Barry W. Boehm. But due to the lack of values for the constant parameters in this model, it cannot meet the high precision for all software projects. Nowadays, regarding the increasing researches on machine learning algorithms and the success of these studies, in this paper, we have tried to estimate the cost of software projects according to meta-heuristic algorithms. In this paper, Ant Colony Optimization (ACO) and Lorentz transformation have been used as Chaos Optimization Algorithm (COA) and NASA datasets as training and testing sets. To compare and evaluate the results of the proposed method with COCOMO model, MARE is used, and the results show a decline in MARE to 0.078%.