Nothing else matters: what predictive model should I use?
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Mining development repositories to study the impact of collaboration on software systems
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Developer prioritization in bug repositories
Proceedings of the 34th International Conference on Software Engineering
Factors characterizing reopened issues: a case study
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Issue ownership activity in two large software projects
ACM SIGSOFT Software Engineering Notes
An industrial study on the risk of software changes
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Categorizing bugs with social networks: a case study on four open source software communities
Proceedings of the 2013 International Conference on Software Engineering
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Correcting software defects accounts for a significant amount of resources such as time, money and personnel. To be able to focus testing efforts where needed the most, researchers have studied statistical models to predict in which parts of a software future defects are likely to occur. By studying the mathematical relations between predictor variables used in these models, researchers can form an increased understanding of the important connections between development activities and software quality. Predictor variables used in past top-performing models are largely based on file-oriented measures, such as source code and churn metrics. However, source code is the end product of numerous interlaced and collaborative activities carried out by developers. Traces of such activities can be found in the repositories used to manage development efforts. In this paper, we investigate statistical models, to study the impact of social structures between developers and end-users on software quality. These models use predictor variables based on social information mined from the issue tracking and version control repositories of a large open-source software project. The results of our case study are promising and indicate that statistical models based on social information have a similar degree of explanatory power as traditional models. Furthermore, our findings suggest that social information does not substitute, but rather augments traditional product and process-based metrics used in defect prediction models.