Generating Bug Reports Using Topic-Modelling and Sentimental Analysis

User reviews play a crucial role in software troubleshooting, offering developers insights into user experiences, feature requests, and bug reports. Apps with a high number of downloads receive a lot of reviews, and developers usually need to analyze and address them effectively. However, manually navigating through a large volume of reviews can be time-consuming and inefficient. As such, there is a need for automated techniques and tools that can assist in the analysis and categorization of reviews. This study leverages topic modeling and sentimental analysis to cluster reviews into topics and identify the sentiments associated with them. The study demonstrates how the two techniques can be utilized together to summarize reviews and identify potential bug reports. User reviews for two popular mobile apps are analyzed using topic modeling and sentimental analysis tools known as BERTopic and VADER (Valence Aware Dictionary and sEntiment Reasoner). Our findings highlight the effectiveness of topic modeling in summarizing reviews, enabling developers to focus on specific issues. The use of sentimental analysis provided insights into the sentiment expressed by users, which will enable developers to focus on addressing widely reported issues. By leveraging these techniques, developers can efficiently prioritize and address issues, leading to improved software quality.