Publication: Poster: Predicting components for issue reports using deep learning with information retrieval
Issued Date
2018-05-27
Resource Type
ISSN
02705257
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2-s2.0-85049674458
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Mahidol University
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SCOPUS
Bibliographic Citation
Proceedings - International Conference on Software Engineering. (2018), 244-245
Suggested Citation
Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Trang Pham, Aditya Ghose Poster: Predicting components for issue reports using deep learning with information retrieval. Proceedings - International Conference on Software Engineering. (2018), 244-245. doi:10.1145/3183440.3194952 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/45635
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Title
Poster: Predicting components for issue reports using deep learning with information retrieval
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Abstract
© 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.