Predict the required cost to develop Software Engineering projects by Using Machine Learning

Abstract

Software project cost prediction is a very important task during building and developing software projects. This process helps software project engineers to accurately manage and plan their resources in terms of cost estimation. However, Need for accurate cost development prediction model for a software project is not a simple procedure. Predicting the cost required while developing software engineering projects is the most difficult challenge that attracts the attention of researchers and practitioners. This paper adopts a new model in estimating the cost of building or developing software engineering projects using a machine learning approach. The results proves that machine learning methods can be used to predict program cost with high accuracy rate compared with traditional software estimation techniques. The proposed model in this research was trained on the NASA (National Aeronautics and Space Administration) data set, which contains the characteristics of 60 projects in addition to the real cost of the projects. An analysis of the results of the implementation for the proposed methods showed that the cost Predicting process using K-Nearest Neighbours algorithm (KNN), Cascade Neural Networks (CNN) and Elman Neural Networks (ENN) It has the ability to predict the costs required to build or develop software engineering projects, K-Nearest Neighbours algorithm has shown high accuracy for Predict the required cost to develop Software Engineering projects Compared to Cascade Neural Networks and Elman Neural Networks ENN.