@proceedings {1491,
	title = {Communication in Open Source Software Development Mailing Lists},
	year = {2013},
	note = {"The entire dataset used in the experiment, including the cards, the resolved aliases, and detailed statistical results, can be downloaded from ..." http://www.st.ewi.tudelft.nl/~guzzi/oss-communication/},
	month = {05/2013},
	pages = {277-286},
	abstract = {Open source software (OSS) development teams use
electronic means, such as emails, instant messaging, or forums,
to conduct open and public discussions. Researchers investigated
mailing lists considering them as a hub for project communication. Prior work focused on specific aspects of emails, for example
the handling of patches, traceability concerns, or social networks.
This led to insights pertaining to the investigated aspects, but not
to a comprehensive view of what developers communicate about.
Our objective is to increase the understanding of development
mailing lists communication.
We quantitatively and qualitatively analyzed a sample of 506
email threads from the development mailing list of a major OSS
project, Lucene. Our investigation reveals that implementation
details are discussed only in about 35\% of the threads, and that
a range of other topics is discussed. Moreover, core developers
participate in less than 75\% of the threads. We observed that the
development mailing list is not the main player in OSS project
communication, as it also includes other channels such as the
issue repository.},
	keywords = {email, lucene, mailling list},
	url = {http://www.st.ewi.tudelft.nl/~guzzi/downloads/Guzzi2013msr.pdf},
	attachments = {https://flosshub.org/sites/flosshub.org/files/Guzzi2013msr.pdf},
	author = {Guzzi, Anja and Bacchelli, Alberto and Lanza, Michele and Pinzger, Martin and van Deursen, Arie}
}
@proceedings {1561,
	title = {A Replicable Infrastructure for Empirical Studies of Email Archives},
	year = {2013},
	month = {10/2013},
	pages = {43-50},
	publisher = {IEEE},
	address = {Baltimore, MD, USA},
	abstract = {This paper describes a replicable infrastructure solution for conducting empirical software engineering studies based on email mailing list archives. Mailing list emails, such as those affiliated with free, libre, and open source software (FLOSS) projects, are currently archived in several places online, but each research team that wishes to study these email artifacts closely must design their own solution for collection, storage and cleaning of the data. Consequently, research results will be difficult to replicate, especially as the email archive for any living project will still be continually growing. This paper describes a simple, replicable infrastructure for the collection, storage, and cleaning of project email data and analyses.},
	keywords = {apache, cleaning, collection, couchdb, database, document-oriented database, email, lucene, mailing lists, nosql, replication, storage},
	isbn = {978-0-7695-5121-0},
	attachments = {https://flosshub.org/sites/flosshub.org/files/RESERv2.pdf},
	author = {Squire, Megan}
}
@conference {950,
	title = {An extensive comparison of bug prediction approaches},
	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 = {31 - 41},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Cape Town, South Africa},
	abstract = {Reliably predicting software defects is one of software engineering{\textquoteright}s holy grails. Researchers have devised and implemented a plethora of bug prediction approaches varying in terms of accuracy, complexity and the input data they require. However, the absence of an established benchmark makes it hard, if not impossible, to compare approaches. We present a benchmark for defect prediction, in the form of a publicly available data set consisting of several software systems, and provide an extensive comparison of the explanative and predictive power of well-known bug prediction approaches, together with novel approaches we devised. Based on the results, we discuss the performance and stability of the approaches with respect to our benchmark and deduce a number of insights on bug prediction models.},
	keywords = {apache, bug reports, eclipse, famix, lucene, mylyn, prediction, scm},
	isbn = {978-1-4244-6802-7},
	doi = {10.1109/MSR.2010.5463279},
	attachments = {https://flosshub.org/sites/flosshub.org/files/31dambrosLanzaRobbes31.pdf},
	author = {D{\textquoteright}Ambros, Marco and Lanza, Michele and Robbes, Romain}
}
@conference {991,
	title = {How Long Will It Take to Fix This Bug?},
	booktitle = {Fourth International Workshop on Mining Software Repositories (MSR{\textquoteright}07:ICSE Workshops 2007)},
	year = {2007},
	pages = {1},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Minneapolis, MN, USA},
	abstract = {Predicting the time and effort for a software problem has long been a difficult task. We present an approach that automatically predicts the fixing effort, i.e., the person-hours spent on fixing an issue. Our technique leverages existing issue tracking systems: given a new issue report, we use the Lucene framework to search for similar, earlier reports and use their average time as a prediction. Our approach thus allows for early effort estimation, helping in assigning issues and scheduling stable releases. We evaluated our approach using effort data from the JBoss project. Given a sufficient number of issues reports, our automatic predictions are close to the actual effort; for issues that are bugs, we are off by only one hour, beating naive predictions by a factor of four.},
	keywords = {bug fixing, bug reports, effort estimation, jboss, lucene, prediction, time},
	isbn = {0-7695-2950-X},
	doi = {10.1109/MSR.2007.13},
	attachments = {https://flosshub.org/sites/flosshub.org/files/28300001.pdf},
	author = {Weiss, Cathrin and Premraj, Rahul and Zimmermann, Thomas and Zeller, Andreas}
}
