| Abstrakt: |
During the maintenance phase of software development, bug reports provide important information for software developers. Developers share information, discuss bugs, and fix associated bugs through bug reports; however, bug reports often include complex and long discussions, and developers have difficulty obtaining the desired information. To address this issue, researchers proposed methods for summarizing bug reports; however, to select relevant sentences, existing methods rely solely on word frequencies or other factors that are dependent on the characteristics of a bug report, failing to produce high-quality summaries or resulting in limited applicability. In this paper, we propose a deep-learning-based bug report summarization method using sentence significance factors. When conducting experiments over a public dataset using believability, sentence-to-sentence cohesion, and topic association as sentence significance factors, the results show that our method outperforms the state-of-the-art method BugSum with respect to precision, recall, and F-score and that the application scope of the proposed method is wider than that of BugSum. [ABSTRACT FROM AUTHOR] |
