Predicting Software Stage Effort with Sequence Changing Ratio
Song Qin · Chinese Journal of Computers · 2009
Software stage effort has the features of data starvation and uncertainty. It is difficult to use the current methods (e.g. regression) to make predictions. This paper proposes a novel prediction method,which gets the effort sequence feature— changing from the completed stage effort sequences,and gets the changing ratio threshold from historical projects by machine learning methods,then uses grey models to make predictions. The experimental results on 10 real world software engineering datasets show that,compared with linear regression method,the prediction accuracy of the proposed method has been improved by 20%~80%. This is very encouraging and indicates that the method has considerable potential.