The impact of parameter tuning on software effort estimation using learning machines
Liyan Song, Leandro Lei Minku, Xin Yao · 2013
Background: The use of machine learning approaches for software effort estimation (SEE) has been studied for more than a decade. Most studies performed comparisons of different learning machines on a number of data sets. However, most learning machines have more than one parameter that needs to be tuned, and it is unknown to what extent parameter settings may affect their performance in SEE. Many works seem to make an implicit assumption that parameter settings would not change the outcomes significantly.