  Crossp-roject defect prediction (CPDP), where data from different software
projects are used to predict defects, has been proposed as a way to provide
data for software projects that lack historical data. Evaluations of CPDP
models using the Nearest Neighbour (NN) Filter approach have shown promising
results in recent studies. A key challenge with defect-prediction datasets is
class imbalance, that is highly skewed datasets where non buggy modules
dominate the buggy modules. In the past, data resampling approaches have been
applied to within-projects defect prediction models to help alleviate the
negative effects of class imbalance in the datasets. To address the class
imbalance issue in CPDP, the authors assess the impact of data resampling
approaches on CPDP models after the NN Filter is applied. The impact on
prediction performance of five oversampling approaches (MAHAKIL, SMOTE,
Borderline-SMOTE, Random Oversampling, and ADASYN) and three undersampling
approaches (Random Undersampling, Tomek Links, and Onesided selection) is
investigated and results are compared to approaches without data resampling.
The authors' examined six defect prediction models on 34 datasets extracted
from the PROMISE repository. The authors results show that there is a
significant positive effect of data resampling on CPDP performance, suggesting
that software quality teams and researchers should consider applying data
resampling approaches for improved recall (pd) and g-measure prediction
performance. However if the goal is to improve precision and reduce false alarm
(pf) then data resampling approaches should be avoided.
