Predicting more from less: Synergies of learning

Thanks to the ever increasing importance of project data, its collection has been one of the primary focuses of software organizations. Data collection activities have resulted in the availability of massive amounts of data through software data repositories. This is great news for the predictive modeling research in software engineering. However, widely used supervised methods for predictive modeling require labeled data that is relevant to the local context of a project. This requirement cannot be met by many of the available data sets, introducing new challenges for software engineering research. How to transfer data between different contexts? How to handle insufficient number of labeled instances? In this position paper, we investigate synergies between different learning methods (transfer, semi-supervised and active learning) which may overcome these challenges.