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.

@inproceedings{paperbot5087,
  title = {Maximal multi-layer specification synthesis},
  author = {Yanju Chen and Ruben Martins and Yu Feng},
  booktitle = {FSE},
  year = {2019},
  doi = {10.1145/3338906.3338951}
}