Software Cost Estimation using Stacked Ensemble Classifier and Feature Selection
Mustafa Hammad · International Journal of Advanced Computer Science and Applications · 2023
Predicting the cost of the development effort is essential for successful projects. This helps software project managers to allocate resources, and determine budget or delivery date. This paper evaluates a set of machine learning algorithms and techniques in predicting the development cost of software projects. A feature selection algorithm is utilized to enhance the accuracy of the prediction process. A set of evaluations are presented based on basic classifiers and stacked ensemble classifiers with and without the feature selection approach. The evaluation study uses a dataset from 76 university students' software projects. Results show that using a stacked ensemble classifier and feature selection technique can increase the accuracy of software cost prediction models.