TY - GEN
T1 - Mining twitter messages for software evolution
AU - Guzman, Emitza
AU - Ibrahim, Mohamed
AU - Glinz, Martin
PY - 2017/6/30
Y1 - 2017/6/30
N2 - Twitter is a widely used social network. Previous research showed that users engage in Twitter to communicate about software applications via short messages, referred to as tweets, and that some of these tweets are relevant for software evolution. However, a manual analysis is impractical due to the large number of tweets - in the range of thousands per day for popular apps. In this work we present ALERTme, an approach to automatically classify, group and rank tweets about software applications. We apply machine learning techniques for automatically classifying tweets requesting improvements, topic modeling for grouping semantically related tweets and a weighted function for ranking tweets according to their relevance for software evolution. We ran our approach on 68,108 tweets from three different software applications and compared the results against practitioners' assessments. Our results are promising and could help incorporate short, informal user feedback with social components into the software evolution process.
AB - Twitter is a widely used social network. Previous research showed that users engage in Twitter to communicate about software applications via short messages, referred to as tweets, and that some of these tweets are relevant for software evolution. However, a manual analysis is impractical due to the large number of tweets - in the range of thousands per day for popular apps. In this work we present ALERTme, an approach to automatically classify, group and rank tweets about software applications. We apply machine learning techniques for automatically classifying tweets requesting improvements, topic modeling for grouping semantically related tweets and a weighted function for ranking tweets according to their relevance for software evolution. We ran our approach on 68,108 tweets from three different software applications and compared the results against practitioners' assessments. Our results are promising and could help incorporate short, informal user feedback with social components into the software evolution process.
KW - Software evolution
KW - Text mining
KW - User feedback
UR - https://www.scopus.com/pages/publications/85026776112
UR - https://www.scopus.com/inward/citedby.url?scp=85026776112&partnerID=8YFLogxK
U2 - 10.1109/ICSE-C.2017.65
DO - 10.1109/ICSE-C.2017.65
M3 - Conference contribution
AN - SCOPUS:85026776112
T3 - Proceedings - 2017 IEEE/ACM 39th International Conference on Software Engineering Companion, ICSE-C 2017
SP - 283
EP - 284
BT - Proceedings - 2017 IEEE/ACM 39th International Conference on Software Engineering Companion, ICSE-C 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE/ACM International Conference on Software Engineering Companion, ICSE-C 2017
Y2 - 20 May 2017 through 28 May 2017
ER -