@conference {bird2009pat,
	title = {{Putting it All Together: Using Socio-Technical Networks to Predict Failures}},
	booktitle = {Proceedings of the 17th International Symposium on Software Reliability Engineering},
	year = {2009},
	note = {First, we build each type of network separately and use network analysis on both to gather metrics for use in a predictive model. Second, we build a socio-technical network which combines the nodes and edges from both the dependency network and the contribution network and use metrics gathered from this network in a predictive model.
We evaluate our approach by collecting data from Mi- crosoft Windows Vista and ECLIPSE development and using logistic regression analysis.},
	abstract = {Studies have shown that social factors in development organizations have a dramatic effect on software quality. Separately, program dependency information has also been used successfully to predict which software components are more fault prone. Interestingly, the influence of these two phenomena have only been studied separately. Intuition and practical experience suggests, however, that task assignment (i.e. who worked on which components and how much) and dependency structure (which components have dependencies on others) together interact to influence the quality of the resulting software. We study the influence of combined socio-technical software networks on the fault-proneness of individual software components within a system. The network properties of a software component in this combined network are able to predict if an entity is failure prone with greater accuracy than prior methods which use dependency or contribution information in isolation. We evaluate our approach in different settings by using it on Windows Vista and across six releases of the Eclipse development environment including using models built from one release to predict failure prone components in the next release. We compare this to previous work. In every case, our method performs as well or better and is able to more accurately identify those software components that have more post-release failures, with precision and recall rates as high as 85\%.},
	keywords = {eclipse, microsoft, social network, vista, windows},
	attachments = {https://flosshub.org/sites/flosshub.org/files/bird2009pat.pdf},
	author = {Christian Bird and Nachiappan Nagappan and Devanbu, Premkumar and Gall, Harald and Brendan Murphy}
}
@conference {Spinellis:2008:TFK:1368088.1368140,
	title = {A tale of four kernels},
	booktitle = {Proceedings of the 30th international conference on Software engineering},
	series = {ICSE {\textquoteright}08},
	year = {2008},
	pages = {381{\textendash}390},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {The FreeBSD, GNU/Linux, Solaris, and Windows operating systems have kernels that provide comparable facilities. Interestingly, their code bases share almost no common parts, while their development processes vary dramatically. We analyze the source code of the four systems by collecting metrics in the areas of file organization, code structure, code style, the use of the C preprocessor, and data organization. The aggregate results indicate that across various areas and many different metrics, four systems developed using wildly different processes score comparably. This allows us to posit that the structure and internal quality attributes of a working, non-trivial software artifact will represent first and foremost the engineering requirements of its construction, with the influence of process being marginal, if any.},
	keywords = {comparison, freebsd, linux, open source, opensolaris, proprietary software, windows, wrk},
	isbn = {978-1-60558-079-1},
	doi = {10.1145/1368088.1368140},
	url = {http://doi.acm.org/10.1145/1368088.1368140},
	author = {Diomidis Spinellis}
}
