@conference {1859,
	title = {OSSEAN: Mining Crowd Wisdom in Open Source Communities},
	booktitle = {2015 IEEE Symposium on Service-Oriented System Engineering (SOSE)},
	year = {2015},
	pages = {367 - 371},
	publisher = {IEEE},
	organization = {IEEE},
	address = {San Francisco Bay, CA, USA},
	abstract = {Nowadays open source software represents a successful crowd-based software production model and is becoming an ecosystem combining huge amounts of software producers (such as software developers) and consumers (such as software users and customers). Lots of research work has been conducted on analyzing software artifacts created by producers, but few of them reveal the power of feedback from consumers which we believe is very important for the evaluation and evolution of open source software. This paper introduces OSSEAN, a platform for Open Source Software Evaluating, Analyzing and Networking. OSSEAN divides the open source communities into two groups: software production communities and software consumption communities. The former contain structured software artifacts such as projects, source code and issues, while the latter are full of textual documents with rich semantics of user feedback. We show the power of OSSEAN with some interesting demos by analyzing more than 200 thousands of open source projects and 10 million documents.},
	keywords = {flossmole},
	doi = {10.1109/SOSE.2015.51},
	author = {Yin, Gang and Wang, Tao and Wang, Huaimin and Fan, Qiang and Zhang, Yang and Yu, Yue and Yang, Cheng}
}
@proceedings {1766,
	title = {Wait For It: Determinants of Pull Request Evaluation Latency on GitHub},
	year = {2015},
	month = {05/2015},
	publisher = {IEEE},
	abstract = {The pull-based development model, enabled by git
and popularised by collaborative coding platforms like BitBucket,
Gitorius, and GitHub, is widely used in distributed software
teams. While this model lowers the barrier to entry for potential
contributors (since anyone can submit pull requests to any repository),
it also increases the burden on integrators (i.e., members
of a project{\textquoteright}s core team, responsible for evaluating the proposed
changes and integrating them into the main development line),
who struggle to keep up with the volume of incoming pull
requests. In this paper we report on a quantitative study that tries
to resolve which factors affect pull request evaluation latency
in GitHub. Using regression modeling on data extracted from
a sample of GitHub projects using the Travis-CI continuous
integration service, we find that latency is a complex issue,
requiring many independent variables to explain adequately.},
	url = {https://bvasiles.github.io/papers/msr15.pdf},
	attachments = {https://flosshub.org/sites/flosshub.org/files/msr15.pdf},
	author = {Yu, Yue and Wang, Huaimin and Filkov, Vladimir and Devanbu, Premkumar and Vasilescu, Bogdan}
}
@conference {Zhang:2014:ISM:2666539.2666572,
	title = {Investigating Social Media in GitHub{\textquoteright}s Pull-requests: A Case Study on Ruby on Rails},
	booktitle = {Proceedings of the 1st International Workshop on Crowd-based Software Development Methods and Technologies},
	series = {CrowdSoft 2014},
	year = {2014},
	pages = {37{\textendash}41},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {In GitHub, pull-request mechanism is an outstanding social development method by integrating with many social media. Many studies have explored that social media has an important effect on software development. @-mention as a typical social media, is a useful tool in social platform. In this paper, we made a quantitative analysis of @-mention in pull-requests of the project Ruby on Rails. First, we make a convictive statistics of the popularity of pull-request mechanism in GitHub. Then we investigate the current situation of @-mention in the Ruby on Rails. Our empirical analysis results find some insights of @-mention.
},
	keywords = {@-mention, github, pull-request, social media},
	isbn = {978-1-4503-3224-8},
	doi = {10.1145/2666539.2666572},
	url = {http://doi.acm.org/10.1145/2666539.2666572},
	author = {Zhang, Yang and Yin, Gang and Yu, Yue and Wang, Huaimin}
}
@conference {1687,
	title = {Recommending relevant projects via user behaviour: an exploratory study on github},
	booktitle = {Proceedings of the 1st International Workshop on Crowd-based Software Development Methods and Technologies - CrowdSoft 2014},
	year = {2014},
	pages = {25 - 30},
	publisher = {ACM Press},
	organization = {ACM Press},
	address = {Hong Kong, ChinaNew York, New York, USA},
	abstract = {Social coding sites (e.g., Github) provide various features like Forking and Sending Pull-requests to support crowd-based software engineering. When using these features, a large amount of user behavior data is recorded. User behavior data can reflect developers preferences and interests in software development activities. Online service providers in many fields have been using user behavior data to discover user preferences and interests to achieve various purposes. In the field of software engineering however, there has been few studies in mining large amount of user behavior data. Our goal is to design an approach based on user behavior data, to recommend relevant open source projects to developers, which can be helpful in activities like searching for the right open source solutions to quickly build prototypes. In this paper, we explore the possibilities of such a method by conducting a set of experiments on selected data sets from Github. We find it a promising direction in mining projects{\textquoteright} relevance from user behavior data. Our study also obtain some important issues that is worth considering in this method.},
	isbn = {9781450332248},
	doi = {10.1145/2666539.2666570},
	url = {http://dl.acm.org/citation.cfm?id=2666570},
	author = {Zhang, Lingxiao and Yanzhen Zou and Bing Xie and Zixiao Zhu}
}
