Abstract
Constructing an accurate effort prediction model is a challenge in software engineering. The development and
validation of models that are used for prediction tasks require good quality data. Unfortunately, software engineering datasets tend
to suffer from the incompleteness which could result to inaccurate decision making and project management and implementation.
Recently, the use of machine learning algorithms has proven to be of great practical value in solving a variety of software
engineering problems including software prediction, including the use of ensemble (combining) classifiers. Research indicates
that ensemble individual classifiers lead to a significant improvement in classification performance by having them vote for the
most popular class. This paper proposes a method for improving software effort prediction accuracy produced by a decision
tree learning algorithm and by generating the ensemble using two imputation methods as elements. Benchmarking results on
ten industrial datasets show that the proposed ensemble strategy has the potential to improve prediction accuracy compared to
an individual imputation method, especially if multiple imputation is a component of the ensemble.