Enhancing Automated Analysis of Bug Descriptions and Report Generation Using Machine Learning & NLP (International Journal of Scientific Research in Science and Technology, 2024)
Bug reports can provide a great deal of assistance for developers during the process of development. But due to the large size of bug repositories, it is sometimes difficult to take advantage of these artifacts in the available time. One way of helping developers to provide summaries of these reports and provide relevant details only. Once it’s decided that this is the required report then one can study the details. As text mining technology advances, many substantial approaches have been proposed to generate optimized summaries for bug reports. In this project, we have proposed an extractive based methodology for the generation of summaries of bug reports by using the sentence embedding. We achieved improved rouge-1 and rouge2 results than the previous state of the art systems for the bug report summary generation.