Is It a Bug or an Enhancement? A Text-based Approach to Classify Change Requests. Antoniol, G., Ayari, K., Di Penta, M., Khomh, F., & Gu�h�neuc, Y. In Vigder, M. & Chechik, M., editors, Proceedings of the 18<sup>th</sup> IBM Centers for Advanced Studies Conference (CASCON), pages 23–37, October, 2008. ACM Press. 15 pages. \awardMost influential paper at CASCON'18.
Paper abstract bibtex Bug tracking systems are valuable assets for managing maintenance activities. They are widely used in open-source projects as well as in the software industry. They collect many different kinds of issues: requests for defect fixing, enhancements, refactoring/restructuring activities and organizational issues. These different kinds of issues are simply labeled as ``bug" for lack of a better classification support or of knowledge about the possible kinds. This paper investigates whether the text of the issues posted in bug tracking systems is enough to classify them into corrective maintenance and other kinds of activities. We show that alternating decision trees, naive Bayes classifiers, and logistic regression can be used to accurately distinguish bugs from other kinds of issues. Results from empirical studies performed on issues for Mozilla, Eclipse, and JBoss indicate that issues can be classified with between 77\NOand 82\NOof correct decisions.
@INPROCEEDINGS{Antoniol08-CASCON-ClassificationofChangeReq,
AUTHOR = {Giuliano Antoniol and Kamel Ayari and
Di Penta, Massimiliano and Foutse Khomh and Yann-Ga�l Gu�h�neuc},
BOOKTITLE = {Proceedings of the 18<sup>th</sup> IBM Centers for Advanced Studies Conference (CASCON)},
TITLE = {Is It a Bug or an Enhancement? A Text-based Approach to
Classify Change Requests},
YEAR = {2008},
OPTADDRESS = {},
OPTCROSSREF = {},
EDITOR = {Mark Vigder and Marsha Chechik},
MONTH = {October},
NOTE = {15 pages. \awardMost influential paper at CASCON'18.},
OPTNUMBER = {},
OPTORGANIZATION = {},
PAGES = {23--37},
PUBLISHER = {ACM Press},
OPTSERIES = {},
OPTVOLUME = {},
KEYWORDS = {Topic: <b>Evolution patterns</b>, Venue: <i>CASCON</i>},
URL = {http://www.ptidej.net/publications/documents/CASCON08.doc.pdf},
PDF = {http://www.ptidej.net/publications/documents/CASCON08.ppt.pdf},
ABSTRACT = {Bug tracking systems are valuable assets for managing
maintenance activities. They are widely used in open-source projects
as well as in the software industry. They collect many different
kinds of issues: requests for defect fixing, enhancements,
refactoring/restructuring activities and organizational issues. These
different kinds of issues are simply labeled as ``bug" for lack of a
better classification support or of knowledge about the possible
kinds. This paper investigates whether the text of the issues posted
in bug tracking systems is enough to classify them into corrective
maintenance and other kinds of activities. We show that alternating
decision trees, naive Bayes classifiers, and logistic regression can
be used to accurately distinguish bugs from other kinds of issues.
Results from empirical studies performed on issues for Mozilla,
Eclipse, and JBoss indicate that issues can be classified with
between 77\NOand 82\NOof correct decisions.}
}
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