  Software defect prediction heavily relies on the metrics collected from
software projects. Earlier studies often used machine learning techniques to
build, validate, and improve bug prediction models using either a set of
metrics collected within a project or across different projects. However,
techniques applied and conclusions derived by those models are restricted by
how identical those metrics are. Knowledge coming from those models will not be
extensible to a target project if no sufficient overlapping metrics have been
collected in the source projects. To explore the feasibility of transferring
knowledge across projects without common labeled metrics, we systematically
integrated Heterogeneous Defect Prediction (HDP) by replicating and validating
the obtained results. Our main goal is to extend prior research and explore the
feasibility of HDP and finally to compare its performance with that of its
predecessor, Cross-Project Defect Prediction. We construct an HDP model on
different publicly available datasets. Moreover, we propose a new ensemble
voting approach in the HDP context to utilize the predictive power of multiple
available datasets. The result of our experiment is comparable to that of the
original study. However, we also explored the feasibility of HDP in real cases.
Our results shed light on the infeasibility of many cases for the HDP algorithm
due to its sensitivity to the parameter selection. In general, our analysis
gives a deep insight into why and how to perform transfer learning from one
domain to another, and in particular, provides a set of guidelines to help
researchers and practitioners to disseminate knowledge to the defect prediction
domain.
