@proceedings {1825,
	title = {Externalization of Software Behavior by the Mining of Norms},
	year = {2016},
	month = {05/2016},
	pages = {223-234},
	abstract = {Open Source Software Development (OSSD) often suffers from conflicting views and actions due to the perceived flat and open ecology of an open source community. This often manifests itself as a lack of codified knowledge that is easily accessible for community members. How decisions are made and expectations of a software system are often described in detail through the many forms of social communications that take place within a community. These social interactions form norms which are influential in dictating what behaviors are expected in a community and of the system. In this paper, we provide a tool which mines these social interactions (in the form of bug reports) and extract norms of the system, externalizing this information into a codified form that allows others within the community to be aware of without having witnessed the social interactions.},
	doi = {http://dx.doi.org/10.1145/2901739.2901744},
	author = {Daniel Avery and Dam, Hoa Khanh and Savarimuthu, Bastin Tony Roy and Aditya Ghose}
}
@proceedings {1774,
	title = {Characterization and prediction of issue-related risks in software projects},
	year = {2015},
	month = {05/2015},
	publisher = {IEEE},
	abstract = {Identifying risks relevant to a software project and
planning measures to deal with them are critical to the success
of the project. Current practices in risk assessment mostly rely
on high-level, generic guidance or the subjective judgements
of experts. In this paper, we propose a novel approach to
risk assessment using historical data associated with a software
project. Specifically, our approach identifies patterns of past
events that caused project delays, and uses this knowledge to
identify risks in the current state of the project. A set of risk
factors characterizing {\textquotedblleft}risky{\textquotedblright} software tasks (in the form of
issues) were extracted from five open source projects: Apache,
Duraspace, JBoss, Moodle, and Spring. In addition, we performed
feature selection using a sparse logistic regression model to
select risk factors with good discriminative power. Based on
these risk factors, we built predictive models to predict if an
issue will cause a project delay. Our predictive models are able
to predict both the risk impact (i.e. the extend of the delay)
and the likelihood of a risk occurring. The evaluation results
demonstrate the effectiveness of our predictive models, achieving
on average 48\%{\textendash}81\% precision, 23\%{\textendash}90\% recall, 29\%{\textendash}71\%
F-measure, and 70\%{\textendash}92\% Area Under the ROC Curve. Our
predictive models also have low error rates: 0.39{\textendash}0.75 for Macroaveraged
Mean Cost-Error and and 0.7{\textendash}1.2 for Macro-averaged
Mean Absolute Error},
	url = {http://www.uow.edu.au/~hoa/papers/msr-2015-preprint.pdf},
	attachments = {https://flosshub.org/sites/flosshub.org/files/msr-2015-preprint.pdf},
	author = {Morakot Choetkiertikul and Dam, Hoa Khanh and Truyen Tran and Aditya Ghose}
}
@proceedings {1728,
	title = {Mining Software Repositories for Social Norms},
	volume = {2},
	year = {2015},
	pages = {627-630},
	abstract = {Social norms facilitate coordination and cooperation
among individuals, thus enable smoother functioning of social
groups such as the highly distributed and diverse open source
software development (OSSD) communities. In these communities,
norms are mostly implicit and hidden in huge records
of human-interaction information such as emails, discussions
threads, bug reports, commit messages and even source code. This
paper aims to introduce a new line of research on extracting social
norms from the rich data available in software repositories. Initial
results include a study of coding convention violations in JEdit,
ArgoUML and Glassfish projects. It also presents a new lifecycle
model for norms in OSSD communities and demonstrates
how a number of norms extracted from the Python development
community follow this life-cycle model.},
	author = {Dam, Hoa Khanh and Savarimuthu, Bastin Tony Roy and Daniel Avery and Aditya Ghose}
}
