  With the rapid growth of software scale and complexity, a large number of bug
reports are submitted to the bug tracking system. In order to speed up defect
repair, these reports need to be accurately classified so that they can be sent
to the appropriate developers. However, the existing classification methods
only use the text information of the bug report, which leads to their low
performance. To solve the above problems, this paper proposes a new automatic
classification method for bug reports. The innovation is that when categorizing
bug reports, in addition to using the text information of the report, the
intention of the report (i.e. suggestion or explanation) is also considered,
thereby improving the performance of the classification. First, we collect bug
reports from four ecosystems (Apache, Eclipse, Gentoo, Mozilla) and manually
annotate them to construct an experimental data set. Then, we use Natural
Language Processing technology to preprocess the data. On this basis, BERT and
TF-IDF are used to extract the features of the intention and the multiple text
information. Finally, the features are used to train the classifiers. The
experimental result on five classifiers (including K-Nearest Neighbor, Naive
Bayes, Logistic Regression, Support Vector Machine, and Random Forest) show
that our proposed method achieves better performance and its F-Measure achieves
from 87.3% to 95.5%.
