@conference {1263,
	title = {Multiple Social Networks Analysis of FLOSS Projects using Sargas},
	booktitle = {2009 42nd Hawaii International Conference on System Sciences (HICSS 2009)},
	year = {2009},
	note = {"The interval of analysis for the first three networks was from 2003/02/24 to 2003/11/03. These networks were created using the OSSNetwork tool [18]. The source code of the PMD project was collected starting on 2002/06/24..."
"Transflow performs a co-changes analysis of the source code modification history and creates a matrix where software components are connected by taking into account the frequency that they have been changed together (i.e., in the same check-in): if two files have been changed together in the same check-in, an edge is created to link these two files."},
	pages = {1 - 10},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Waikoloa, Hawaii, USA},
	abstract = {Due to their characteristics and claimed advantages, several researchers have been investigating free and open-source projects. Different aspects are being studied: for instance, what motivates developers to join FLOSS projects, the tools, processes and practices used in FLOSS projects, the evolution of FLOSS communities among other things. Researchers have studied collaboration and coordination of open source software developers using an approach known as social network analysis and have gained important insights about these projects. Most researchers, however, have not focused on the integrated study of these networks and, accordingly, in their interrelationships. This paper describes an approach and tool to combine multiple social networks to study the evolution of open-source projects. Our tool, named Sargas, allows comparison and visualization of different social networks at the same time. Initial results of our analysis can be used to extend the "onion-model" of open source participation.},
	keywords = {case study, multiple social networks, ossnetwork, pmd, social network analysis, transflow},
	isbn = {978-0-7695-3450-3},
	doi = {10.1109/HICSS.2009.316},
	attachments = {https://flosshub.org/sites/flosshub.org/files/07-07-06.pdf},
	author = {de Sousa, S.F. and Balieiro, M.A. and dos R. Costa, J.M. and de Souza, C.R.B.}
}
@conference {1012,
	title = {Prioritizing Warning Categories by Analyzing Software History},
	booktitle = {Fourth International Workshop on Mining Software Repositories (MSR{\textquoteright}07:ICSE Workshops 2007)},
	year = {2007},
	pages = {27 - 27},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Minneapolis, MN, USA},
	abstract = {Automatic bug finding tools tend to have high false positive rates: most warnings do not indicate real bugs. Usually bug finding tools prioritize each warning category. For example, the priority of "overflow" is 1 and the priority of "jumbled incremental" is 3, but the tools{\textquoteright} prioritization is not very effective.

In this paper, we prioritize warning categories by analyzing the software change history. The underlying intuition is that if warnings from a category are resolved quickly by developers, the warnings in the category are important. Experiments with three bug finding tools (FindBugs, JLint, and PMD) and two open source projects (Columba and jEdit) indicate that different warning categories have very different lifetimes. Based on that observation, we propose a preliminary algorithm for warning category prioritizing.},
	keywords = {bug fixing, change history, columba, findbugs, jedit, jlint, kenyon, pmd, warning},
	isbn = {0-7695-2950-X},
	doi = {10.1109/MSR.2007.26},
	attachments = {https://flosshub.org/sites/flosshub.org/files/28300027.pdf},
	author = {Kim, Sunghun and Ernst, Michael D.}
}
@conference {Xie:2006:MMA:1137983.1137997,
	title = {MAPO: mining API usages from open source repositories},
	booktitle = {Proceedings of the 2006 international workshop on Mining software repositories},
	series = {MSR {\textquoteright}06},
	year = {2006},
	pages = {54{\textendash}57},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {To improve software productivity, when constructing new software systems, developers often reuse existing class libraries or frameworks by invoking their APIs. Those APIs, however, are often complex and not well documented, posing barriers for developers to use them in new client code. To get familiar with how those APIs are used, developers may search the Web using a general search engine to find relevant documents or code examples. Developers can also use a source code search engine to search open source repositories for source files that use the same APIs. Nevertheless, the number of returned source files is often large. It is difficult for developers to learn API usages from a large number of returned results. In order to help developers understand API usages and write API client code more effectively, we have developed an API usage mining framework and its supporting tool called MAPO (for Mining API usages from Open source repositories). Given a query that describes a method, class, or package for an API, MAPO leverages the existing source code search engines to gather relevant source files and conducts data mining. The mining leads to a short list of frequent API usages for developers to inspect. MAPO currently consists of five components: a code search engine, a source code analyzer, a sequence preprocessor, a frequent sequence miner, and a frequent sequence post processor. We have examined the effectiveness of MAPO using a set of various queries. The preliminary results show that the framework is practical for providing informative and succinct API usage patterns.},
	keywords = {api, application programming interfaces, documentation, mining software repositories, pmd, program comprehension, search engine, sequences, source code, source code search engine},
	isbn = {1-59593-397-2},
	doi = {http://doi.acm.org/10.1145/1137983.1137997},
	url = {http://doi.acm.org/10.1145/1137983.1137997},
	attachments = {https://flosshub.org/sites/flosshub.org/files/54MAPO.pdf},
	author = {Xie, Tao and Pei, Jian}
}
