A Study on Software Effort Prediction Using Machine Learning Techniques

This paper conducts a study on of software effort prediction using machine learning techniques. Both supervised and unsupervised learning techniques are employed to predict software effort using historical dataset. The unsupervised learning as k-medoids clustering equipped with different similarity measures is used to cluster projects in historical dataset. The supervised learning as J48 decision tree, back propagation neural network (BPNN) and naive Bayes is used to classify the software projects into different effort classes. We also impute the missing values in the historical datasets and then machine learning techniques are adopted to predict software effort. Experiments on ISBSG and CSBSG datasets demonstrate that unsupervised learning as k-medoids clustering produced a poor performance. Kulzinsky coefficient has the best performance in measuring the similarities of projects. Supervised learning techniques produced superior performances than unsupervised learning techniques in software effort prediction. BPNN produced the best performance among the three supervised learning techniques. Missing data imputation improved the performances of both unsupervised and supervised learning techniques in software effort prediction.