SPLASH 2020
Sun 15 - Sat 21 November 2020 Online Conference
Fri 20 Nov 2020 11:00 - 11:20 at SPLASH-III - F-3B Chair(s): Yaniv David, Francisco Ferreira
Fri 20 Nov 2020 23:00 - 23:20 at SPLASH-III - F-3B Chair(s): Dimitri Racordon, Yulei Sui

A key challenge in program synthesis is the astronomical size of the search space the synthesizer has to explore. In response to this challenge, recent work proposed to guide synthesis using learned probabilistic models. Obtaining such a model, however, might be infeasible for a problem domain where no high-quality training data is available.
In this work we introduce an alternative approach to guided program synthesis: instead of training a model ahead of time we show how to bootstrap one just in time, during synthesis,
by learning from partial solutions encountered along the way. To make the best use of the model, we also propose a new program enumeration algorithm we dub guided bottom-up search,
which extends the efficient bottom-up search with guidance from probabilistic models.

We implement this approach in a tool called Probe, which targets problems in the popular syntax-guided synthesis (SyGuS) format. We evaluate Probe on benchmarks from the literature
and show that it achieves significant performance gains both over unguided bottom-up search
and over a state-of-the-art probability-guided synthesizer, which had been trained on a corpus of existing solutions. Moreover, we show that these performance gains do not come at the cost of solution quality: programs generated by Probe are only slightly more verbose than the shortest solutions and perform no unnecessary case-splitting.

Fri 20 Nov

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11:00 - 12:20
F-3BOOPSLA at SPLASH-III +12h
Chair(s): Yaniv David Technion, Francisco Ferreira Imperial College London
11:00
20m
Talk
Just-in-Time Learning for Bottom-Up Enumerative Synthesis
OOPSLA
Shraddha Barke University of California at San Diego, Hila Peleg University of California at San Diego, Nadia Polikarpova University of California at San Diego
Link to publication DOI Media Attached
11:20
20m
Talk
Taming Type Annotations in Gradual Typing
OOPSLA
John Peter Campora University of Louisiana at Lafayette, Sheng Chen University of Louisiana at Lafayette
Link to publication DOI Media Attached
11:40
20m
Talk
Learning Semantic Program Embeddings with Graph Interval Neural NetworkDistinguished Paper
OOPSLA
Yu Wang Nanjing University, Ke Wang Visa Research, Fengjuan Gao Nanjing University, Linzhang Wang Nanjing University
Link to publication DOI Media Attached
12:00
20m
Talk
ιDOT: A DOT Calculus with Object Initialization
OOPSLA
Ifaz Kabir University of Alberta, Yufeng Li University of Waterloo, Ondřej Lhoták University of Waterloo
Link to publication DOI Media Attached
23:00 - 00:20
F-3BOOPSLA at SPLASH-III
Chair(s): Dimitri Racordon University of Geneva, Switzerland, Yulei Sui University of Technology Sydney
23:00
20m
Talk
Just-in-Time Learning for Bottom-Up Enumerative Synthesis
OOPSLA
Shraddha Barke University of California at San Diego, Hila Peleg University of California at San Diego, Nadia Polikarpova University of California at San Diego
Link to publication DOI Media Attached
23:20
20m
Talk
Taming Type Annotations in Gradual Typing
OOPSLA
John Peter Campora University of Louisiana at Lafayette, Sheng Chen University of Louisiana at Lafayette
Link to publication DOI Media Attached
23:40
20m
Talk
Learning Semantic Program Embeddings with Graph Interval Neural NetworkDistinguished Paper
OOPSLA
Yu Wang Nanjing University, Ke Wang Visa Research, Fengjuan Gao Nanjing University, Linzhang Wang Nanjing University
Link to publication DOI Media Attached
00:00
20m
Talk
ιDOT: A DOT Calculus with Object Initialization
OOPSLA
Ifaz Kabir University of Alberta, Yufeng Li University of Waterloo, Ondřej Lhoták University of Waterloo
Link to publication DOI Media Attached