DC Field | Value | Language |
---|---|---|
dc.contributor.author | Koh, Youngji | ko |
dc.contributor.author | Kang, Sungwon | ko |
dc.contributor.author | Lee, Seonah | ko |
dc.date.accessioned | 2022-07-06T06:01:23Z | - |
dc.date.available | 2022-07-06T06:01:23Z | - |
dc.date.created | 2022-07-05 | - |
dc.date.created | 2022-07-05 | - |
dc.date.created | 2022-07-05 | - |
dc.date.issued | 2022-06 | - |
dc.identifier.citation | APPLIED SCIENCES-BASEL, v.12, no.12 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | http://hdl.handle.net/10203/297273 | - |
dc.description.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. | - |
dc.language | English | - |
dc.publisher | MDPI | - |
dc.title | Deep Learning-Based Bug Report Summarization Using Sentence Significance Factors | - |
dc.type | Article | - |
dc.identifier.wosid | 000816627800001 | - |
dc.identifier.scopusid | 2-s2.0-85132187818 | - |
dc.type.rims | ART | - |
dc.citation.volume | 12 | - |
dc.citation.issue | 12 | - |
dc.citation.publicationname | APPLIED SCIENCES-BASEL | - |
dc.identifier.doi | 10.3390/app12125854 | - |
dc.contributor.localauthor | Kang, Sungwon | - |
dc.contributor.nonIdAuthor | Lee, Seonah | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | bug report | - |
dc.subject.keywordAuthor | bug tracking system | - |
dc.subject.keywordAuthor | data-based software engineering | - |
dc.subject.keywordAuthor | text summarization | - |
dc.subject.keywordAuthor | open source software | - |
dc.subject.keywordAuthor | sentence significance factor | - |
dc.subject.keywordAuthor | software maintenance | - |
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