BERT based severity prediction of bug reports for the maintenance of mobile applications

Mobile application maintenance is crucial to ensuring the accurate operation and continuous improvement of mobile applications (mobile apps). To effectively address issues and enhance the user experience, developers utilize issue-tracking systems that gather bug reports to refine mobile apps. Users can submit bugs through these systems, allowing them to determine the severity of each reported issue. The severity level plays a pivotal role in prioritizing bug resolution, enabling developers to address critical bugs promptly. Nonetheless, manually assessing the severity of each issue can be laborious and prone to errors. To overcome this challenge, this paper presents Bidirectional Encoder Representations from Transformers (BERT) based severity prediction of bug reports (called BERT-SBR) that leverages a deep neural network for automatic bug severity classification for mobile app maintenance. We collect the publicly available mobile apps bug reports dataset from the Hugging Face. BERT-SBR first computes the sentiment of reporters of bug reports and preprocesses them by leveraging BertTokenizer input formatting techniques. Next, it passes the formatted text and computed sentiment of each bug report to generate word embeddings. Then, it introduces a fine-tuned BERT classifier for bug report severity prediction. After that, it passes the generated word embeddings to the fine-tuned BERT classifier for training and testing. Finally, the proposed classifier's performance is evaluated. The BERT-SBR assessment results confirm that the fine-tuned BERT classifies bug reports significantly more effectively than other deep learning classifiers. On average, BERT-SBR achieves a remarkable improvement of 40.43%, 67.78%, 40.71%, and 58.14% in the accuracy, precision, recall, and f-measure. This indicates its superiority in accurately predicting the severity of bug reports for mobile application maintenance.