Representation learning for software engineering and programming languages

Recently, deep learning (DL) and machine learning (ML) methods have been massively and successfully applied in various software engineering (SE) and programming languages (PL) tasks. The results are promising and exciting, and lead to further opportunities of exploring the amenability of DL and ML to different SE and PL tasks. Notably, the choice of the representations on which DL and ML methods are applied critically impacts the performance of the DL and ML methods. The rapidly developing field of representation learning (RL) in artificial intelligence is concerned with questions surrounding how we can best learn meaningful and useful representations of data. A broad view of the RL in SE and PL can include the topics, e.g., deep learning, feature learning, compositional modeling, structured prediction, and reinforcement learning. This workshop will advance the pace of research in the unique intersection of representation learning and SE and PL, which will, in the long term, lead to more effective solutions to common software engineering tasks such as coding, maintenance, testing, and porting. In addition to attracting the community of researchers who usually attend FSE, we have made intensive efforts to attract researchers from the RL (broadly AI) community to the workshop, specially from local, very strong groups in local universities, and research labs in the nation.