@INPROCEEDINGS{fse19-mars,
  title     = "Maximal {Multi-Layer} Specification Synthesis",
  booktitle = "Proceedings of the 2019 27th {ACM} Joint Meeting on European
               Software Engineering Conference and Symposium on the Foundations
               of Software Engineering",
  author    = "Chen, Yanju and Martins, Ruben and Feng, Yu",
  abstract  = "There has been a significant interest in applying
               programming-by-example to automate repetitive and tedious tasks.
               However, due to the incomplete nature of input-output examples,
               a synthesizer may generate programs that pass the examples but
               do not match the user intent. In this paper, we propose MARS, a
               novel synthesis framework that takes as input a multi-layer
               specification composed by input-output examples, textual
               description, and partial code snippets that capture the user
               intent. To accurately capture the user intent from the noisy and
               ambiguous description, we propose a hybrid model that combines
               the power of an LSTM-based sequence-to-sequence model with the
               apriori algorithm for mining association rules through
               unsupervised learning. We reduce the problem of solving a
               multi-layer specification synthesis to a Max-SMT problem, where
               hard constraints encode well-typed concrete programs and soft
               constraints encode the user intent learned by the hybrid model.
               We instantiate our hybrid model to the data wrangling domain and
               compare its performance against Morpheus, a state-of-the-art
               synthesizer for data wrangling tasks. Our experiments
               demonstrate that our approach outperforms MORPHEUS in terms of
               running time and solved benchmarks. For challenging benchmarks,
               our approach can suggest candidates with rankings that are an
               order of magnitude better than MORPHEUS which leads to running
               times that are 15x faster than MORPHEUS.",
  publisher = "Association for Computing Machinery",
  pages     = "602--612",
  series    = "ESEC/FSE 2019",
  year      =  2019,
  address   = "New York, NY, USA",
  keywords  = "program synthesis, neural networks, machine learning, Max-SMT",
  location  = "Tallinn, Estonia"
}