Constrained Program Inference Using Metropolis-Hastings Sampling

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dc.contributor.advisorJermaine, Christopheren_US
dc.creatorChilukuri, Meghana Vasudha Orisen_US
dc.date.accessioned2021-05-03T19:29:28Zen_US
dc.date.available2021-05-03T19:29:28Zen_US
dc.date.created2021-05en_US
dc.date.issued2021-04-30en_US
dc.date.submittedMay 2021en_US
dc.date.updated2021-05-03T19:29:28Zen_US
dc.description.abstractConditional Program Generation (CPG) is a Sketch program generation technique that uses clues given by the user to generate the required Sketch. The CPG model generates a Sketch by sampling from the learned probability distribution P(sketch|clues). However, it cannot guarantee that the generated Sketch will incorporate all of the given clues. In practice, we find that when the CPG model assigns vanishingly low probabilities to every Sketch program in the Sketch program space that incorporates all of the given clues, it often returns a high-probability Sketch program that does not contain the clues. Such scenarios arise when the user wants to generate a novel program and gives clues that are significantly different from any set of clues the model was trained on. In this thesis, we introduce Constrained Program Inference (CPI), a method that treats constrained Sketch program generation as an inference problem, rather than a training problem. It guarantees every generated program will incorporate all of the given clues. Our method uses the Metropolis- Hastings algorithm to treat clues as hard constraints, thus enabling CPI to generate novel programs. We find that CPI is able to produce higher-quality programs than CPG.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationChilukuri, Meghana Vasudha Oris. "Constrained Program Inference Using Metropolis-Hastings Sampling." (2021) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/110375">https://hdl.handle.net/1911/110375</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/110375en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectprogram synthesisen_US
dc.subjectmetropolis-hastingsen_US
dc.subjectsketch generationen_US
dc.titleConstrained Program Inference Using Metropolis-Hastings Samplingen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.majorProgram Synthesisen_US
thesis.degree.nameMaster of Scienceen_US
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