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. Attached Files Name Description MIMEType Size Downloads Title Poster: Predicting components for issue reports using deep learning with information retrieval Author(s) Choetkiertikul, M Dam, HK 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) Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 01 Apr 2019, 14:10:08 EST Tue, 02 Apr 2019, 04:01:07 EST Tue, 02 Apr 2019, 05:05:00 EST Tue, 02 Apr 2019, 13:14:51 EST Sun, 07 Nov 2021, 12:11:55 EST Sun, 07 Nov 2021, 13:11:57 EST Sun, 07 Nov 2021, 13:33:19 EST Filtered Full Citation counts:   Cited 3 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 20 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Mon, 01 Apr 2019, 14:10:08 EST