  Context: Cross-project defect prediction (CPDP) models are being developed to
optimize the testing resources. Objectives: Proposing an ensemble
classification framework for CPDP as many existing models are lacking with
better performances and analysing the main objectives of CPDP from the outcomes
of the proposed classification framework. Method: For the classification task,
we propose a bootstrap aggregation based hybrid-inducer ensemble learning
(HIEL) technique that uses probabilistic weighted majority voting (PWMV)
strategy. To know the impact of HIEL on the software project, we propose three
project-specific performance measures such as percent of perfect cleans (PPC),
percent of non-perfect cleans (PNPC), and false omission rate (FOR) from the
predictions to calculate the amount of saved cost, remaining service time, and
percent of the failures in the target project. Results: On many target projects
from PROMISE, NASA, and AEEEM repositories, the proposed model outperformed
recent works such as TDS, TCA+, HYDRA, TPTL, and CODEP in terms of F-measure.
In terms of AUC, the TCA+ and HYDRA models stand as strong competitors to the
HIEL model. Conclusion: For better predictions, we recommend ensemble learning
approaches for the CPDP models. And, to estimate the benefits from the CPDP
models, we recommend the above project-specific performance measures.
