USER-GENERATED BUG REPORTS USING TOPIC MODELLING AND SENTIMENTAL ANALYSIS
Author(s):
Dr. Bhavesh M. Patel, M. Sule
Keywords:
NLP Tools, topic modeling, sentimental analysis
Abstract
Bug reports are a valuable resource for software troubleshooting and bug fixes, however, the process of obtaining such reports is cumbersome and expensive. One of the effective channels of receiving bug reports is from user reviews, these often show how end users of the app feel about features and bugs. But the problem with manually iterating through a bunch of user reviews and feedback is that it is time-consuming and inefficient. This study deploys a state-of-the-art technique using topic modeling and sentimental analysis to cluster user reviews into manageable topics and identify the sentiments behind them. The study reveals how topic modeling and sentimental analysis can be useful techniques for digging into user reviews to find bug reportsand spot serious issues that could have a negative impact on user experience. Byemploying these techniques, software developers can better interpret user feedback efficiently and prioritize their bug-fixing efforts accordingly. User reviews related to bugs in mobile applications are examined using NLP tools such as BERTopic and VADER(Valence Aware Dictionary and sEntiment Reasoner). The research has practical applications for app developers who can use it to improve user experience and make their apps better. By offering new perspectives on the relationship between subjects covered in bug reports from user reviews and the sentiments behind them, this study adds to the increasing body of knowledge on the efficient generation of bug reports, topic modeling, and sentiment analysis.
Article Details
Unique Paper ID: 161889

Publication Volume & Issue: Volume 10, Issue 6

Page(s): 430 - 436
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