Identifying misclassified bug reports

DOI

10.2991/msmee-17.2017.273How to use a DOI?

Keywords

bug reports, classification rules, misclassification.

Abstract

The data mining methodology for identification and detection of bugs is an important application. Especially separating bugs from non-bugs is a general challenge. When software developers classify bug reports, they may misclassify bug reports with bias and errors. All issue reports are analyzed by combining the classification rules from open-ihm project and Herzig et al. (2012). A comparison on the classification results of different authors has been extracted which shows the misclassification rate. Depending on the percent of misclassification rates, it is concluded that the classification of bugs in Herzig et al. (2012) can be applied to the open-ihm project and it exhibited the similar proportion of misclassified bugs as reported in Herzig et al. (2012).

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Suo Hu
AU  - Zhou Zou
PY  - 2017/05
DA  - 2017/05
TI  - Identifying misclassified bug reports
BT  - Proceedings of the 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017)
PB  - Atlantis Press
SP  - 1514
EP  - 1520
SN  - 2352-5401
UR  - https://doi.org/10.2991/msmee-17.2017.273
DO  - 10.2991/msmee-17.2017.273
ID  - Hu2017/05
ER  -