  Data-driven defect prediction has become increasingly important in software
engineering process. Since it is not uncommon that data from a software project
is insufficient for training a reliable defect prediction model, transfer
learning that borrows data/knowledge from other projects to facilitate the
model building at the current project, namely cross-project defect prediction
(CPDP), is naturally plausible. Most CPDP techniques involve two major steps,
i.e., transfer learning and classification, each of which has at least one
parameter to be tuned to achieve their optimal performance. This practice fits
well with the purpose of automated parameter optimization. However, there is a
lack of thorough understanding about what are the impacts of automated
parameter optimization on various CPDP techniques. In this paper, we present
the first empirical study that looks into such impacts on 62 CPDP techniques,
13 of which are chosen from the existing CPDP literature while the other 49
ones have not been explored before. We build defect prediction models over 20
real-world software projects that are of different scales and characteristics.
Our findings demonstrate that: (1) Automated parameter optimization
substantially improves the defect prediction performance of 77\% CPDP
techniques with a manageable computational cost. Thus more efforts on this
aspect are required in future CPDP studies. (2) Transfer learning is of
ultimate importance in CPDP. Given a tight computational budget, it is more
cost-effective to focus on optimizing the parameter configuration of transfer
learning algorithms (3) The research on CPDP is far from mature where it is
"not difficult" to find a better alternative by making a combination of
existing transfer learning and classification techniques. This finding provides
important insights about the future design of CPDP techniques.
