This paper introduces a set of datasets grouped under the name Turkish
Software Bug Reports (TSBR), which comprises commercial software bug
reports from a closed-source project. We investigate and report the
statistical properties and classification difficulty of the TSBR
datasets. We employ various methods from the text classification
literature to apply several classification tasks related to software
development on the TSBR datasets. The methods we employ include
traditional machine learning (ML) methods such as k-nearest neighbors
(KNN) and random forest (RF); sequential deep learning (DL) models such
as gated recurrent unit (GRU) and convolutional neural network (CNN);
transformer-based language models; and ensembles of the employed models.
Our work is among the first efforts in automated bug report
classification literature that uses ensembles of DL models.