Automated Summarization of Bug Reports to Speedup Software Development/Maintenance Process by Using Natural Language Processing (NLP)

Abstract

Developers may benefit much from bug reports as they work on new features. However, it might be challenging to make use of these artifacts in the given time owing to the massive size of bug repositories. One strategy to aid developers is to give concise summaries of these reports, focusing on the most relevant information. After deciding if this report is what's needed, you may go into the specifics. With the development of text mining tools, several considerable methods have been developed to produce efficient summaries for bug reports. In this research, we present an extractive-based technique that makes use of language embedding to generate summaries of bug reports. In comparison to prior state-of-the-art methods, our rouge-1 and rouge-2 outcomes for bug report summarization are far better.