@conference {Saini:2014:DMA:2597073.2597134,
	title = {A Dataset for Maven Artifacts and Bug Patterns Found in Them},
	booktitle = {Proceedings of the 11th Working Conference on Mining Software Repositories},
	series = {MSR 2014},
	year = {2014},
	pages = {416{\textendash}419},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {In this paper, we present data downloaded from Maven, one of the most popular component repositories. The data includes the binaries of 186,392 components, along with source code for 161,025. We identify and organize these components into groups where each group contains all the versions of a library. In order to asses the quality of these components, we make available report generated by the FindBugs tool on 64,574 components. The information is also made available in the form of a database which stores total number, type, and priority of bug patterns found in each component, along with its defect density. We also describe how this dataset can be useful in software engineering research.
},
	keywords = {Empirical Research, Empirical software engineering, findbugs, maven, software quality},
	isbn = {978-1-4503-2863-0},
	doi = {10.1145/2597073.2597134},
	url = {http://doi.acm.org/10.1145/2597073.2597134},
	author = {Saini, Vaibhav and Sajnani, Hitesh and Ossher, Joel and Lopes, Cristina V.}
}
@conference {Gousios:2014:DPD:2597073.2597122,
	title = {A Dataset for Pull-based Development Research},
	booktitle = {Proceedings of the 11th Working Conference on Mining Software Repositories},
	series = {MSR 2014},
	year = {2014},
	pages = {368{\textendash}371},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Pull requests form a new method for collaborating in distributed software development. To study the pull request distributed development model, we constructed a dataset of almost 900 projects and 350,000 pull requests, including some of the largest users of pull requests on Github. In this paper, we describe how the project selection was done, we analyze the selected features and present a machine learning tool set for the R statistics environment.
},
	keywords = {Distributed software development, Empirical software engineering, msr data showcase, pull request, pull-based development},
	isbn = {978-1-4503-2863-0},
	doi = {10.1145/2597073.2597122},
	url = {http://doi.acm.org/10.1145/2597073.2597122},
	attachments = {https://flosshub.org/sites/flosshub.org/files/pullreqs-dataset.pdf},
	author = {Gousios, Georgios and Zaidman, Andy}
}
@conference {1357,
	title = {Content classification of developer emails},
	booktitle = {Proceedings of the 34th IEEE/ACM International Conference On Software Engineering (ICSE 2012)},
	year = {2012},
	note = {We created a web application to manually classify email content in the chosen categories. We classified a statistically significant set of emails from four java open source software (OSS) systems, used to evaluate the accuracy of our approach.
The contributions of this paper are:
1) a novel approach that fuses parsing and ML techniques
for classification of email lines;
2) a web application to manually classify email content; 3) the manual classification of a statistically significant
sample set of emails (for a total of 67,792 lines) from mailing lists of four different software systems{\textendash}in the form of a freely available benchmark; and
4) the empirical evaluation of our approach against the benchmark},
	month = {06/2012},
	abstract = {Emails related to the development of a software system contain information about design choices and issues encountered during the development process. Exploiting the knowledge embedded in emails with automatic tools is challenging, due to the unstructured, noisy and mixed language nature of this communication medium. Natural language text is often not well-formed and is interleaved with languages with other syntaxes, such as code or stack traces.

We present an approach to classify email content at line level. Our technique classifies email lines in five categories (i.e., text, junk, code, patch, and stack trace) to allow one to subsequently apply ad hoc analysis techniques for each category. We evaluated our approach on a statistically significant set of emails gathered from mailing lists of four unrelated open source systems.},
	keywords = {email, Emails, Empirical software engineering, mailing list, natural language, Unstructured Data Mining},
	url = {http://www.inf.usi.ch/phd/bacchelli/publications.php},
	attachments = {https://flosshub.org/sites/flosshub.org/files/icse2012.pdf},
	author = {Bacchelli, Alberto and Dal Sasso, Tommaso and D{\textquoteright}Ambros, Marco and Lanza, Michele}
}
