%0 Conference Paper %B Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work &\#38; Social Computing %D 2014 %T How Social Q&A Sites Are Changing Knowledge Sharing in Open Source Software Communities %A Vasilescu, Bogdan %A Serebrenik, Alexander %A Devanbu, Prem %A Filkov, Vladimir %K a %K crowdsourced knowledge %K gamification. %K mailing lists %K open source %K social q&\#38 %X Historically, mailing lists have been the preferred means for coordinating development and user support activities. With the emergence and popularity growth of social Q&A sites such as the StackExchange network (e.g., StackOverflow), this is beginning to change. Such sites offer different socio-technical incentives to their participants than mailing lists do, e.g., rich web environments to store and manage content collaboratively, or a place to showcase their knowledge and expertise more vividly to peers or potential recruiters. A key difference between StackExchange and mailing lists is gamification, i.e., StackExchange participants compete to obtain reputation points and badges. In this paper, we use a case study of R (a widely-used tool for data analysis) to investigate how mailing list participation has evolved since the launch of StackExchange. Our main contribution is the assembly of a joint data set from the two sources, in which participants in both the texttt{r-help} mailing list and StackExchange are identifiable. This permits their activities to be linked across the two resources and also over time. With this data set we found that user support activities show a strong shift away from texttt{r-help}. In particular, mailing list experts are migrating to StackExchange, where their behaviour is different. First, participants active both on texttt{r-help} and on StackExchange are more active than those who focus exclusively on only one of the two. Second, they provide faster answers on StackExchange than on texttt{r-help}, suggesting they are motivated by the emph{gamified} environment. To our knowledge, our study is the first to directly chart the changes in behaviour of specific contributors as they migrate into gamified environments, and has important implications for knowledge management in software engineering. %B Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work &\#38; Social Computing %S CSCW '14 %I ACM %C New York, NY, USA %P 342–354 %@ 978-1-4503-2540-0 %U http://doi.acm.org/10.1145/2531602.2531659 %R 10.1145/2531602.2531659 %> https://flosshub.org/sites/flosshub.org/files/cscw14.pdf %0 Conference Paper %B 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010) %D 2010 %T Validity of network analyses in Open Source Projects %A Nia, Roozbeh %A Christian Bird %A Devanbu, Premkumar %A Filkov, Vladimir %K apache %K email archives %K mailing lists %K missing data %K mysql %K perl %K social networks %X Social network methods are frequently used to analyze networks derived from Open Source Project communication and collaboration data. Such studies typically discover patterns in the information flow between contributors or contributions in these projects. Social network metrics have also been used to predict defect occurrence. However, such studies often ignore or side-step the issue of whether (and in what way) the metrics and networks of study are influenced by inadequate or missing data. In previous studies email archives of OSS projects have provided a useful trace of the communication and co-ordination activities of the participants. These traces have been used to construct social networks that are then subject to various types of analysis. However, during the construction of these networks, some assumptions are made, that may not always hold; this leads to incomplete, and sometimes incorrect networks. The question then becomes, do these errors affect the validity of the ensuing analysis? In this paper we specifically examine the stability of network metrics in the presence of inadequate and missing data. The issues that we study are: 1) the effect of paths with broken information flow (i.e. consecutive edges which are out of temporal order) on measures of centrality of nodes in the network, and 2) the effect of missing links on such measures. We demonstrate on three different OSS projects that while these issues do change network topology, the metrics used in the analysis are stable with respect to such changes. %B 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010) %I IEEE %C Cape Town, South Africa %P 201 - 209 %@ 978-1-4244-6802-7 %R 10.1109/MSR.2010.5463342 %> https://flosshub.org/sites/flosshub.org/files/201NetworkAnalysis.pdf