TY - GEN
T1 - Poster
T2 - 40th ACM/IEEE International Conference on Software Engineering, ICSE 2018
AU - Choetkiertikul, Morakot
AU - Dam, Hoa Khanh
AU - Tran, Truyen
AU - Pham, Trang
AU - Ghose, Aditya
N1 - Publisher Copyright:
© 2018 Authors.
PY - 2018/5/27
Y1 - 2018/5/27
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85049674458
U2 - 10.1145/3183440.3194952
DO - 10.1145/3183440.3194952
M3 - Conference contribution
AN - SCOPUS:85049674458
T3 - Proceedings - International Conference on Software Engineering
SP - 244
EP - 245
BT - Proceedings - International Conference on Software Engineering
PB - IEEE Computer Society
Y2 - 27 May 2018 through 3 June 2018
ER -