Thu 21 Jun 2018 14:00 - 14:25 at Grand Ballroom CD - Synthesis and Learning Chair(s): Xin Zhang

Predicting program properties such as names or expression types has a wide range of applications. It can ease the task of programming, and increase programmer productivity. A major challenge when learning from programs is \emph{how to represent programs in a way that facilitates effective learning}.

We present ageneral path-based representation for learning from programs. Our representation is purely syntactic and extracted automatically. The main idea is to represent a program using paths in its abstract syntax tree (AST). This allows a learning model to leverage the structured nature of code rather than treating it as a flat sequence of tokens.

We show that this representation is general and can:
(i) cover different prediction tasks,
(ii) drive different learning algorithms (for both generative and discriminative models), and
(iii) work across different programming languages.

We evaluate our approach on the tasks of predicting variable names, method names, and full types. We use our representation to drive both CRF-based and word2vec-based learning, for programs of four languages: JavaScript, Java, Python and C#. Our evaluation shows that our approach obtains better results than task-specific handcrafted representations across different tasks and programming languages.

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-papers14:00 - 14:25
Talk
Uri AlonTechnion, Meital ZilbersteinTechnion, Omer LevyUniversity of Washington, USA, Eran YahavTechnion
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pldi-2018-papers14:25 - 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-papers14:50 - 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-papers15:15 - 15:40
Talk
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