A Decision Support System for Estimating Cost of Software Projects Using a Hybrid of Multi-layer Artificial Neural Network and Decision Tree

One of the major challenges for software, nowadays, is software cost estimation. It refers to estimating the cost of all activities including software development, design, supervision, maintenance and so on. Accurate cost-estimation of software projects optimizes the internal and external processes, staff works, efforts and the overheads to be coordinated with one another. In the management software projects, estimation must be taken into account so that reduces costs, timing and possible risks to avoid project failure. In this paper, a decisionsupport system using a combination of multi-layer artificial neural network and decision tree is proposed to estimate the cost of software projects. In the model included into the proposed system, normalizing factors, which is vital in evaluating efforts and costs estimation, is carried out using C4.5 decision tree. Moreover, testing and training factors are done by multi-layer artificial neural network and the most optimal values are allocated to them. The experimental results and evaluations on Dataset NASA60 show that the proposed system has less amount of the total average relative error compared with COCOMO model.