TY - GEN
T1 - Analyzing emotion words to predict severity of software bugs
T2 - 32nd Annual ACM Symposium on Applied Computing, SAC 2017
AU - Yang, Geunseok
AU - Baek, Seungsuk
AU - Lee, Jung Won
AU - Lee, Byungjeong
N1 - Publisher Copyright:
Copyright 2017 ACM.
PY - 2017/4/3
Y1 - 2017/4/3
N2 - A successful software development project becomes an essential part of a software company's reputation. Thus, lots of project managers focus more on maintenance than on other management processes. Previous works studied how to help the maintenance process by detecting bug duplication and predicting the severity of bugs. This paper continues that kind of special work by analyzing emotion words for bug-severity prediction. In detail, we construct an emotion words-based dictionary for verifying bug reports' textual emotion analyses based on positive and negative terms. Then, we modify a machine learning algorithm, the Naïve Bayes multinomial, calling the new algorithm EWD-Multinomial. We compare this EWD-Multinomial study with our baselines, including Naïve Bayes multinomial and a Lamkanfi study, for open source projects such as Eclipse, Android, and JBoss. The result shows this study's algorithm outperforms the others.
AB - A successful software development project becomes an essential part of a software company's reputation. Thus, lots of project managers focus more on maintenance than on other management processes. Previous works studied how to help the maintenance process by detecting bug duplication and predicting the severity of bugs. This paper continues that kind of special work by analyzing emotion words for bug-severity prediction. In detail, we construct an emotion words-based dictionary for verifying bug reports' textual emotion analyses based on positive and negative terms. Then, we modify a machine learning algorithm, the Naïve Bayes multinomial, calling the new algorithm EWD-Multinomial. We compare this EWD-Multinomial study with our baselines, including Naïve Bayes multinomial and a Lamkanfi study, for open source projects such as Eclipse, Android, and JBoss. The result shows this study's algorithm outperforms the others.
KW - Bug report
KW - Bug severity prediction
KW - Emotion words-based dictionary
KW - Software maintenance
UR - http://www.scopus.com/inward/record.url?scp=85020901390&partnerID=8YFLogxK
U2 - 10.1145/3019612.3019788
DO - 10.1145/3019612.3019788
M3 - Conference contribution
AN - SCOPUS:85020901390
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 1280
EP - 1287
BT - 32nd Annual ACM Symposium on Applied Computing, SAC 2017
PB - Association for Computing Machinery
Y2 - 4 April 2017 through 6 April 2017
ER -