Thu 21 Jun 2018 14:25 - 14:50 at Grand Ballroom CD - Synthesis and Learning Chair(s): Xin Zhang

We propose a new conflict-driven program synthesis technique that is capable of learning from past mistakes. Given a spurious program that violates the desired specification, our synthesis algorithm identifies the root cause of the conflict and learns new lemmas that can prevent similar mistakes in the future. Specifically, we introduce the notion of equivalence modulo conflict and show how this idea can be used to learn useful lemmas that allow the synthesizer to prune large parts of the search space. We have implemented a general-purpose CDCL-style program synthesizer called Neo and evaluate it in two different application domains, namely data wrangling in R and functional programming over lists. Our experiments demonstrate the substantial benefits of conflict-driven learning and show that Neo outperforms two state-of-the-art synthesis tools, Morpheus and Deepcoder, that target these respective domains.

Thu 21 Jun

pldi-2018-papers
14:00 - 15:40: PLDI Research Papers - Synthesis and Learning at Grand Ballroom CD
Chair(s): Xin ZhangMassachusetts Institute of Technology, USA
pldi-2018-papers152958240000014:00 - 14:25
Talk
Uri AlonTechnion, Meital ZilbersteinTechnion, Omer LevyUniversity of Washington, USA, Eran YahavTechnion
Media Attached
pldi-2018-papers152958390000014:25 - 14:50
Talk
Yu FengUniversity of Texas at Austin, USA, Ruben MartinsCarnegie Mellon University, Osbert BastaniStanford University, Isil DilligUT Austin
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pldi-2018-papers152958540000014:50 - 15:15
Talk
Woosuk LeeUniversity of Pennsylvania, USA, Kihong HeoUniversity of Pennsylvania, USA, Rajeev AlurUniversity of Pennsylvania, Mayur NaikUniversity of Pennsylvania
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pldi-2018-papers152958690000015:15 - 15:40
Talk
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