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Poster: Predicting components for issue reports using deep learning with information retrieval

Choetkiertikul, M, Dam, HK, Tran, Truyen, Pham, Trang Thi Minh and Ghose, A 2018, Poster: Predicting components for issue reports using deep learning with information retrieval, in Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, ACM,, pp. 244-245, doi: 10.1145/3183440.3194952.

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Title Poster: Predicting components for issue reports using deep learning with information retrieval
Author(s) Choetkiertikul, M
Dam, HK
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Pham, Trang Thi Minh
Ghose, A
Conference name 40th International Conference on Software Engineering (ICSE)
Conference location Gothenburg, Sweden
Conference dates 2018/05/27 - 2018/06/03
Title of proceedings Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings
Publication date 2018
Start page 244
End page 245
Total pages 2
Publisher ACM
Keyword(s) Science & Technology
Technology
Computer Science, Software Engineering
Computer Science
Summary © 2018 Authors. Assigning an issue to the correct component(s) is challenging, especially for large-scale projects which have are up to hundreds of components. We propose a prediction model which learns from historical issues reports and recommends the most relevant components for new issues. Our model uses the deep learning Long Short-Term Memory to automatically learns semantic features representing an issue report, and combines them with the traditional textual similarity features. An extensive evaluation on 142,025 issues from 11 large projects shows our approach outperforms alternative techniques with an average 60% improvement in predictive performance.
ISBN 9781450356633
ISSN 0270-5257
DOI 10.1145/3183440.3194952
Indigenous content off
HERDC Research category E3 Extract of paper
Persistent URL http://hdl.handle.net/10536/DRO/DU:30120294

Document type: Conference Paper
Collection: A2I2 (Applied Artificial Intelligence Institute)
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Citation counts: TR Web of Science Citation Count  Cited 3 times in TR Web of Science
Scopus Citation Count Cited 3 times in Scopus Google Scholar Search Google Scholar
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Created: Mon, 01 Apr 2019, 14:10:08 EST

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