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- W4281774036 abstract "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." @default.
- W4281774036 created "2022-06-13" @default.
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- W4281774036 date "2022-06-08" @default.
- W4281774036 modified "2023-09-26" @default.
- W4281774036 title "Deep Learning-Based Bug Report Summarization Using Sentence Significance Factors" @default.
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- W4281774036 doi "https://doi.org/10.3390/app12125854" @default.
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