@proceedings {1508,
	title = {The Eclipse and Mozilla Defect Tracking Dataset: A Genuine Dataset for Mining Bug Information},
	year = {2013},
	note = {Intended to be an "open bug database" or baseline for multiple studies in the community. 14 bug attributes [id, product, summary, status, etc], some change over time and some do not. Data set: github.com/ansymo/msr2013-bug_dataset},
	month = {05/2013},
	abstract = {The analysis of bug reports is an important subfield within the mining software repositories community. It explores the rich data available in defect tracking systems to uncover interesting and actionable information about the bug triaging process. While bug data is readily accessible from systems like Bugzilla and JIRA, a common database schema and a curated dataset could significantly enhance future research because it allows for easier replication. Consequently, in this paper we propose the Eclipse and Mozilla Defect Tracking Dataset, a representative database of bug data, filtered to contain only genuine defects (i.e., no feature requests) and designed to cover the whole bug-triage life cycle (i.e., store all intermediate actions). We have used this dataset ourselves for predicting bug severity, for studying bug-fixing time and for identifying erroneously assigned components. github.com/ansymo/msr2013-bug_dataset},
	url = {http://github.com/ansymo/msr2013-bug_dataset},
	author = {Lamkanfi, Ahmed and Javier P{\'e}rez and Demeyer, Serge}
}
@conference {949,
	title = {Predicting the severity of a reported bug},
	booktitle = {2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)},
	year = {2010},
	pages = {1 - 10},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Cape Town, South Africa},
	abstract = {The severity of a reported bug is a critical factor in deciding how soon it needs to be fixed. Unfortunately, while clear guidelines exist on how to assign the severity of a bug, it remains an inherent manual process left to the person reporting the bug. In this paper we investigate whether we can accurately predict the severity of a reported bug by analyzing its textual description using text mining algorithms. Based on three cases drawn from the open-source community (Mozilla, Eclipse and GNOME), we conclude that given a training set of sufficient size (approximately 500 reports per severity), it is possible to predict the severity with a reasonable accuracy (both precision and recall vary between 0.65-0.75 with Mozilla and Eclipse; 0.70-0.85 in the case of GNOME).},
	keywords = {bug reports, eclipse, gnome, mozilla, severity, text mining},
	isbn = {978-1-4244-6802-7},
	doi = {10.1109/MSR.2010.5463284},
	attachments = {https://flosshub.org/sites/flosshub.org/files/1lamkanfiDemeyer1.pdf},
	author = {Lamkanfi, Ahmed and Demeyer, Serge and Giger, Emanuel and Goethals, Bart}
}
