  Bug reports (BR) contain vital information that can help triaging teams
prioritize and assign bugs to developers who will provide the fixes. However,
studies have shown that BR fields often contain incorrect information that need
to be reassigned, which delays the bug fixing process. There exist approaches
for predicting whether a BR field should be reassigned or not. These studies
use mainly BR descriptions and traditional machine learning algorithms (SVM,
KNN, etc.). As such, they do not fully benefit from the sequential order of
information in BR data, such as function call sequences in BR stack traces,
which may be valuable for improving the prediction accuracy. In this paper, we
propose a novel approach, called EnHMM, for predicting the reassignment of BR
fields using ensemble Hidden Markov Models (HMMs), trained on stack traces.
EnHMM leverages the natural ability of HMMs to represent sequential data to
model the temporal order of function calls in BR stack traces. When applied to
Eclipse and Gnome BR repositories, EnHMM achieves an average precision, recall,
and F-measure of 54%, 76%, and 60% on Eclipse dataset and 41%, 69%, and 51% on
Gnome dataset. We also found that EnHMM improves over the best single HMM by
36% for Eclipse and 76% for Gnome. Finally, when comparing EnHMM to Im.ML.KNN,
a recent approach in the field, we found that the average F-measure score of
EnHMM improves the average F-measure of Im.ML.KNN by 6.80% and improves the
average recall of Im.ML.KNN by 36.09%. However, the average precision of EnHMM
is lower than that of Im.ML.KNN (53.93% as opposed to 56.71%).
