Special section on realizing artificial intelligence synergies in software engineering

With its boundaries expanding into other disciplines and fields as computers become more and more ubiquitous, software engineering (SE) is expected to solve a plethora of increasingly complex questions that are dynamic, automated, and adaptive or must execute at a very large scale. In theory, there could be another layer of collaboration between SE and other disciplines in addition to employing software systems of varying complexity. For example, artificial intelligence (AI) technologies can support the development of increasingly complex SE systems as in the case of recommendation systems. Conversely, in theory, SE might also play a role in alleviating development costs and the development effort associated with AI tools and applications such as robotics where proper development and testing practices are of utmost importance from both cost and robustness perspectives. Unfortunately, in practice, such collaborations between SE and AI are rarely achieved. This special section is the joint result of invited papers from the RAISE’14 Workshop on Realizing Artificial Intelligence Synergies in Software Engineering and an open call for contributions to address the issues above. All submissions were reviewed by experts in the field. Finally, three papers were selected for inclusion in this special section. These papers cover specific AI/SE synergies such as experiments with boosting-based cost-sensitive transfer learning for cross-project defect prediction, a systematic review on the applications of Bayesian networks in software quality estimation, and case studies in learning developer cues in the context of program comprehension. We hope that these papers will be useful in reflecting the type of discussions in the RAISE Workshop series, as well as in providing a view of the state of the art in the research areas covered by each paper. Before we provide brief summaries of the papers, we would like to extend our sincere thanks to the authors for their contributions, reviewers for their help and high quality feedback, and the Editor-in-Chief for making this special issue possible. Software Qual J DOI 10.1007/s11219-017-9356-8