TY - GEN
T1 - The automatic classification of fault trigger based bug report
AU - Du, Xiaoting
AU - Zheng, Zheng
AU - Xiao, Guanping
AU - Yin, Beibei
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/14
Y1 - 2017/11/14
N2 - Understanding the types of defects is of practical interest, which could help developers adopt proper measures in current and future software releases. As the amount of bug reports increasing, manual classification brings a heavy burden to developers. In this paper, we propose a word2vec based framework of multi-granularity automatic classification for bug reports based on fault triggers. Except classifying bug reports into bug/non-bug and Bohrbug/Mandelbug, the classification of Mandelbugs is the focus of this paper. Characteristic representation of common classification methods suffer from data sparsity and high dimensionality, thus we use word2vec, which can express words as low-dimensional word vectors with semantic relations in this paper. Furthermore, in order to improve the quality of classification, we analyzed the impact factors of classification. The results show that our method performs well in automatic classifying bugs into fault trigger classes.
AB - Understanding the types of defects is of practical interest, which could help developers adopt proper measures in current and future software releases. As the amount of bug reports increasing, manual classification brings a heavy burden to developers. In this paper, we propose a word2vec based framework of multi-granularity automatic classification for bug reports based on fault triggers. Except classifying bug reports into bug/non-bug and Bohrbug/Mandelbug, the classification of Mandelbugs is the focus of this paper. Characteristic representation of common classification methods suffer from data sparsity and high dimensionality, thus we use word2vec, which can express words as low-dimensional word vectors with semantic relations in this paper. Furthermore, in order to improve the quality of classification, we analyzed the impact factors of classification. The results show that our method performs well in automatic classifying bugs into fault trigger classes.
KW - Automatic classification
KW - Bug report
KW - Fault trigger
KW - Mandelbug
KW - Word2vec
UR - https://www.scopus.com/pages/publications/85040569610
U2 - 10.1109/ISSREW.2017.28
DO - 10.1109/ISSREW.2017.28
M3 - 会议稿件
AN - SCOPUS:85040569610
T3 - Proceedings - 2017 IEEE 28th International Symposium on Software Reliability Engineering Workshops, ISSREW 2017
SP - 259
EP - 265
BT - Proceedings - 2017 IEEE 28th International Symposium on Software Reliability Engineering Workshops, ISSREW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2017
Y2 - 23 October 2017 through 26 October 2017
ER -