@proceedings {1573,
	title = {"A bit of code": How the Stack Overflow Community Creates Quality Postings},
	year = {2014},
	month = {01/2014},
	pages = {1425-1434},
	publisher = {IEEE Computer Society},
	abstract = {The Stack Overflow web site is an online community where programmers can ask and answer one another{\textquoteright}s questions, earning points and badges. The site offers guidance in the form of a Frequently Asked Questions (FAQ), beginning with "What kind of questions can I ask here?" The answer explains that "the best Stack Overflow questions have a bit of source code in them". This paper explores the role of source code and non-source code text on Stack Overflow in both questions and answers. The primary contribution of this paper is to provide a more detailed understanding of whether the presence of source code (and how much) actually will produce the "best" Stack Overflow questions or answers. A second contribution of this paper is to determine how the non-code portions of the text might also contribute the "best" Stack Overflow postings.  },
	keywords = {COLLABORATION, collaborative development, data mining, developer network, knowledge collaboration, open content, text mining},
	doi = {http://dx.doi.org/10.1109/HICSS.2014.185},
	attachments = {https://flosshub.org/sites/flosshub.org/files/hicssSMFinalWatermark.pdf},
	author = {Squire, Megan and Funkhouser, Christian}
}
@proceedings {1467,
	title = {A Linguistic Analysis on How Contributors Solve Software Problems in a Distributed Context},
	volume = {378},
	year = {2012},
	note = {"... a sample of 4109 bug reports was extracted from Bugzilla bug report repository"
"The analysis of our sample allows identifying the roles of participants in this activity according to their hierarchical statutes in the community. "
"we look statistically at words that people use in Bugzilla to discern differences in the discourse and representation between participants (core and periphery)"},
	month = {09/2012},
	pages = {322-330},
	publisher = {IFIP AICT,  Springer},
	abstract = {There is a little understanding of distributed solving activities in Open Source communities. This study aimed to provide some insights in this way. It was applied to the context of Bugzilla, the bug tracking system of Mozilla community. This study investigated the organizational aspects of this meditated, complex and highly distributed context through a linguistic analysis method. The main finding of this research shows that the organization of distributed problem-solving activities in Bugzilla isn{\textquoteright}t based only on the hierarchical distribution of the work between core and periphery participants but on their implication in the interactions. This implication varies according to the status of each one participant in the community. That is why we distinguish their roles, as well as, the established modes to manage such activity.},
	keywords = {bug report, bugzilla, linguistic, text mining},
	author = {Masmoudi, H{\'e}la and Boughzala, Imed}
}
@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}
}
@conference {Hata:2008:EFF:1370750.1370772,
	title = {An extension of fault-prone filtering using precise training and a dynamic threshold},
	booktitle = {Proceedings of the 2008 international working conference on Mining software repositories},
	series = {MSR {\textquoteright}08},
	year = {2008},
	pages = {89{\textendash}98},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Fault-prone module detection in source code is important for assurance of software quality. Most previous fault-prone detection approaches have been based on software metrics. Such approaches, however, have difficulties in collecting the metrics and in constructing mathematical models based on the metrics. To mitigate such difficulties, we have proposed a novel approach for detecting fault-prone modules using a spam-filtering technique, named Fault-Prone Filtering. In our approach, fault-prone modules are detected in such a way that the source code modules are considered as text files and are applied to the spam filter directly. In practice, we use the training only errors procedure and apply this procedure to fault-prone. Since no pre-training is required, this procedure can be applied to an actual development field immediately. This paper describes an extension of the training only errors procedures. We introduce a precise unit of training, "modified lines of code," instead of methods. In addition, we introduce the dynamic threshold for classification. The result of the experiment shows that our extension leads to twice the precision with about the same recall, and improves 15\% on the best F1 measurement.},
	keywords = {eclipse, fault-prone modules, spam filter, text mining},
	isbn = {978-1-60558-024-1},
	doi = {http://doi.acm.org/10.1145/1370750.1370772},
	url = {http://doi.acm.org/10.1145/1370750.1370772},
	attachments = {https://flosshub.org/sites/flosshub.org/files/p89-hata.pdf},
	author = {Hata, Hideaki and Mizuno, Osamu and Kikuno, Tohru}
}
@conference {993,
	title = {Spam Filter Based Approach for Finding Fault-Prone Software Modules},
	booktitle = {Fourth International Workshop on Mining Software Repositories (MSR{\textquoteright}07:ICSE Workshops 2007)},
	year = {2007},
	pages = {4 - 4},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Minneapolis, MN, USA},
	abstract = {Because of the increase of needs for spam e-mail detection, the spam filtering technique has been improved as a convenient and effective technique for text mining. We propose a novel approach to detect fault-prone modules in a way that the source code modules are considered as text files and are applied to the spam filter directly. In order to show the applicability of our approach, we conducted experimental applications using source code repositories of Java based open source developments. The result of experiments shows that our approach can classify more than 75\% of software modules correctly.},
	keywords = {argouml, bug reports, classification, eclipse, java, modules, scm, spam, text mining},
	isbn = {0-7695-2950-X},
	doi = {10.1109/MSR.2007.29},
	attachments = {https://flosshub.org/sites/flosshub.org/files/28300004.pdf},
	author = {Mizuno, Osamu and Ikami, Shiro and Nakaichi, Shuya and Kikuno, Tohru}
}
@conference {Fischer:2005:MED:1083142.1083145,
	title = {Mining evolution data of a product family},
	booktitle = {Proceedings of the 2005 international workshop on Mining software repositories},
	series = {MSR {\textquoteright}05},
	year = {2005},
	pages = {12-16},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Diversification of software assets through changing requirements impose a constant challenge on the developers and maintainers of large software systems. Recent research has addressed the mining for data in software repositories of single products ranging from fine- to coarse grained analyses. But so far, little attention has been payed to mining data about the evolution of product families. In this work, we study the evolution and commonalities of three variants of the BSD (Berkeley Software Distribution), a large open source operating system. The research questions we tackle are concerned with how to generate high level views of the system discovering and indicating evolutionary highlights. To process the large amount of data, we extended our previously developed approach for storing release history information to support the analysis of product families. In a case study we apply our approach on data from three different code repositories representing about 8.5GB of data and 10 years of active development.},
	keywords = {bsd, change analysis, change history, cvs, evolution, freebsd, netbsd, openbsd, release history, source code, text mining},
	isbn = {1-59593-123-6},
	doi = {http://doi.acm.org/10.1145/1082983.1083145},
	url = {http://doi.acm.org/10.1145/1082983.1083145},
	attachments = {https://flosshub.org/sites/flosshub.org/files/12MiningEvolution.pdf},
	author = {Fischer, Michael and Oberleitner, Johann and Ratzinger, Jacek and Gall, Harald}
}
