%0 Conference Proceedings %B 13th International Conference on Mining Software Repositories (MSR'16) %D 2016 %T Externalization of Software Behavior by the Mining of Norms %A Daniel Avery %A Dam, Hoa Khanh %A Savarimuthu, Bastin Tony Roy %A Aditya Ghose %X 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. %B 13th International Conference on Mining Software Repositories (MSR'16) %P 223-234 %8 05/2016 %R http://dx.doi.org/10.1145/2901739.2901744 %0 Conference Proceedings %B 12th Working Conference on Mining Software Repositories (MSR 2015) %D 2015 %T Characterization and prediction of issue-related risks in software projects %A Morakot Choetkiertikul %A Dam, Hoa Khanh %A Truyen Tran %A Aditya Ghose %X 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 “risky” 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%–81% precision, 23%–90% recall, 29%–71% F-measure, and 70%–92% Area Under the ROC Curve. Our predictive models also have low error rates: 0.39–0.75 for Macroaveraged Mean Cost-Error and and 0.7–1.2 for Macro-averaged Mean Absolute Error %B 12th Working Conference on Mining Software Repositories (MSR 2015) %I IEEE %8 05/2015 %U http://www.uow.edu.au/~hoa/papers/msr-2015-preprint.pdf %> https://flosshub.org/sites/flosshub.org/files/msr-2015-preprint.pdf %0 Conference Proceedings %B 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering %D 2015 %T Mining Software Repositories for Social Norms %A Dam, Hoa Khanh %A Savarimuthu, Bastin Tony Roy %A Daniel Avery %A Aditya Ghose %X 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. %B 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering %V 2 %P 627-630