  Accurate software effort estimation has been a challenge for many software
practitioners and project managers. Underestimation leads to disruption in the
projects estimated cost and delivery. On the other hand, overestimation causes
outbidding and financial losses in business. Many software estimation models
exist; however, none have been proven to be the best in all situations. In this
paper, a decision tree forest (DTF) model is compared to a traditional decision
tree (DT) model, as well as a multiple linear regression model (MLR). The
evaluation was conducted using ISBSG and Desharnais industrial datasets.
Results show that the DTF model is competitive and can be used as an
alternative in software effort prediction.
