@ARTICLE{oopsla22-ngst2,
  title     = "Automated Transpilation of Imperative to Functional Code Using
               {Neural-Guided} Program Synthesis",
  author    = "Mariano, Benjamin and Chen, Yanju and Feng, Yu and Durrett, Greg
               and Dillig, I{\c s}il",
  abstract  = "While many mainstream languages such as Java, Python, and C\#
               increasingly incorporate functional APIs to simplify programming
               and improve parallelization/performance, there are no effective
               techniques that can be used to automatically translate existing
               imperative code to functional variants using these APIs.
               Motivated by this problem, this paper presents a transpilation
               approach based on inductive program synthesis for modernizing
               existing code. Our method is based on the observation that the
               overwhelming majority of source/target programs in this setting
               satisfy an assumption that we call trace-compatibility: not only
               do the programs share syntactically identical low-level
               expressions, but these expressions also take the same values in
               corresponding execution traces. Our method leverages this
               observation to design a new neural-guided synthesis algorithm
               that (1) uses a novel neural architecture called cognate grammar
               network (CGN) and (2) leverages a form of concolic execution to
               prune partial programs based on intermediate values that arise
               during a computation. We have implemented our approach in a tool
               called NGST2 and use it to translate imperative Java and Python
               code to functional variants that use the Stream and functools
               APIs respectively. Our experiments show that NGST2 significantly
               outperforms several baselines and that our proposed neural
               architecture and pruning techniques are vital for achieving good
               results.",
  journal   = "Proc. ACM Program. Lang.",
  publisher = "Association for Computing Machinery",
  volume    =  6,
  number    = "OOPSLA1",
  month     =  apr,
  year      =  2022,
  address   = "New York, NY, USA",
  keywords  = "transpilation, neural networks, program synthesis"
}