Enhancing the software maintenance greatly depends on the precise and prompt handing out of bug reports according to their bug-category and importance. To resolve the aforementioned problems, an automated method of classifying and ranking bug reports is required. Numerous scholars have recently looked into the automated classification and prioritization of bug reports. But not much has been accomplished in this area. During software development, the most crucial stages are testing and maintenance. In these phases of development activity, bug reports are essential. When software modules are being tested, the software quality assurance team creates a bug report. But the main issue that comes up while analysing bug data that is written in normal text. As a result, processing and extracting information from it is extremely challenging. The aforementioned requirements are the driving force for this research. The Proposed research suggested creating a hybrid model that takes advantage of machine learning models' contextual awareness as well as more conventional feature extraction methods (such as TF-IDF). A downstream classifier (such as an SVM, logistic regression) can receive these two feature sets (one from TF-IDF and the other from BERT) after they have been concatenated. This enables the model to take advantage of the extensive contextual relationships that BERT captures as well as the statistical importance of phrases (TF-IDF) These two approaches were used separately in the earlier research, which resulted in less performance. The research made use of a confidential dataset that was acquired from a private company upon request for performing testing, the data included from eight hundred employees. To aid in model training, bug keywords were first taken out of the bug description field. The results shows that proposed model achieves 89% accuracy.
Racharla et al. (2025) studied this question.