  The prediction of defects in a target project based on data from external
projects is called Cross-Project Defect Prediction (CPDP). Several methods have
been proposed to improve the predictive performance of CPDP models. However,
there is a lack of comparison among state-of-the-art methods. Moreover,
previous work has shown that the most suitable method for a project can vary
according to the project being predicted. This makes the choice of which method
to use difficult. We provide an extensive experimental comparison of 31 CPDP
methods derived from state-of-the-art approaches, applied to 47 versions of 15
open source software projects. Four methods stood out as presenting the best
performances across datasets. However, the most suitable among these methods
still varies according to the project being predicted. Therefore, we propose
and evaluate a meta-learning solution designed to automatically select and
recommend the most suitable CPDP method for a project. Our results show that
the meta-learning solution is able to learn from previous experiences and
recommend suitable methods dynamically. When compared to the base methods,
however, the proposed solution presented minor difference of performance. These
results provide valuable knowledge about the possibilities and limitations of a
meta-learning solution applied for CPDP.
