In software engineering there is an active research field of defect prediction using
software metrics. While the research shows that the prediction of defects using software
metrics performs well, prediction using metrics alone lacks clear refactoring capabilities.
On the other hand, code smells have the ability to describe the code anomalies precisely,
and suggest their refactoring. Therefore, code smells can be a much better starting
position for software fault prediction.In this paper, we present the results of preliminary
research on the ability to predict software defects with the code smell God Class.
The aim of our research was to test the definition of God Class, as defined by Lanza
and Marinescu in 2006, in the ability to predict defects in a case study of the open
source projects JDT and PDE within the Eclipse framework. The definition of the God
Class was adapted using the grid search technique, with the goal of maximizing the
fault prediction ability while keeping the base of the original definition. The results
show that adaption of the definition in the specific project resulted in improved
fault prediction ability.