Software enhancement effort estimation using correlation-based feature selection and stacking ensemble method

摘要

Estimating software enhancement efforts became a challenging task in software project management. Recent researches focused on identifying the best machine learning algorithms for software maintenance effort estimation. Most of the research publications investigated the use of ensemble learning for improving software effort estimation. Intending to increase the estimation accuracy over individual models, this paper investigates the use of the stacking ensemble method for estimating the enhancement maintenance effort (EME) of software projects. This paper makes a comparison between two machine learning-based approaches for estimating software EME: The M5P (as an individual model) and the stacking as an ensemble method combining different regression models (GBRegr, LinearSVR, and RFR) using the ISBSG dataset. A correlation-based feature selection (CFS) algorithm is basically used to achieve efficient data reduction. The selected ML techniques-based approaches were trained and tested on a dataset with relevant features leading to the improvement of estimate accuracy. Results show that the software EME estimation using CFS and stacking ensemble method is improved in terms of mean absolute error (MAE) = 0.0383 and root mean square error (RMSE) = 0.1973.