@inproceedings{571ef6e76e4d42e1b071e56a0efa86d3,
title = "Deep Learning-based Production and Test Bug Report Classification using Source Files",
abstract = "Classifying production and test bug reports can significantly improve not only the accuracy of performance evaluation but also the performance of information retrieval-based bug localization (IRBL). However, it is time-consuming for developers to classify these bug reports manually. This study proposes a production and test bug report classification method based on deep learning. Our method uses a set of source files and model tuning to solve the problem of insufficient and sparse bug reports when applying deep learning. Our experimental results reveal that the macro f1-score of our method is 0.84 and can improve the IRBL performance by 20\%.",
keywords = "bug report classification, deep learning, information retrieval-based bug localization, Production bug, test bug",
author = "Misoo Kim and Youngkyoung Kim and Eunseok Lee",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 44th ACM/IEEE International Conference on Software Engineering: Companion, ICSE-Companion 2022 ; Conference date: 22-05-2022 Through 27-05-2022",
year = "2022",
doi = "10.1109/ICSE-Companion55297.2022.9793815",
language = "English",
series = "Proceedings - International Conference on Software Engineering",
publisher = "IEEE Computer Society",
pages = "343--344",
booktitle = "Proceedings - 2022 ACM/IEEE 44th International Conference on Software Engineering",
}