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Program Synthesis using Conflict-Driven Learning

Yu Feng, Ruben Martins, Osbert Bastani, Isil Dillig. Program Synthesis using Conflict-Driven Learning. PLDI'18.

Command-line options

  • app: source of the benchmark in json.
  • depth: size of the sketch
  • learn: enable conflict-driven learning
  • stat: enable statistical model
  • file: source of the ngram ranking provided by Morpheus
  • spec: abstract semantics of the DSL constructs (e.g., gather, spread, mutate, etc).

Neo for DeepCode

Original deepCode: no learning + statistical model:

ant neoDeep -Dapp=./problem/DeepCoder-New/prog13.json -Ddepth=3 -Dlearn=false -Dstat=false -Dfile=""

Neo for Morpheus

Without n-gram information:

ant neoMorpheus -Dapp=./problem/Morpheus/r4.json -Ddepth=3 -Dlearn=false -Dstat=false -Dfile="" -Dspec=specs/Morpheus/

With n-gram information using a file:

ant neoMorpheus -Dapp=./problem/Morpheus/r1.json -Ddepth=3 -Dlearn=true -Dstat=false -Dfile=sketches/ngram-size3.txt -Dspec=specs/Morpheus/

Set up neural net model

requires:

  • Python 2.7
  • NumPy and Tensorflow

The latter can be installed using the following commands:

pip install numpy pip install tensorflow

Then, run org.genesys.clients.DeepCoderDeciderMain to test the Python decider.

If a python interpreter other than the default should be used, then create a text file ./model/tmp/python_path.txt and include the path. For example, to use /usr/local/bin/python, include "/usr/local/bin/" in this file.