Tue 20 Jun 2023 10:20 - 10:40 at Royal - PLDI: Synthesis Chair(s): Ilya Sergey

Synthesis tools have seen significant success in recent times. However,
past approaches often require a complete and accurate embedding of the source
language in the logic of the underlying solver, an approach difficult for
industrial-grade languages. Other approaches couple the semantics of the source
language with purpose-built synthesizers, necessarily tying the synthesis engine
to a particular language model.
In this paper, we propose Absynthe, an alternative approach based on
user-defined abstract semantics that aims to be both lightweight and language
agnostic, yet effective in guiding the search for programs.
A synthesis goal in Absynthe is specified as an abstract
specification in a lightweight user-defined abstract domain and concrete test
The synthesis engine is parameterized by the abstract semantics and independent
of the source language.
Absynthe validates candidate programs against test cases using the
actual concrete language implementation to ensure correctness.
We formalize the synthesis rules for Absynthe
and describe how the key ideas are scaled-up in our implementation in Ruby. We
evaluated Absynthe on SyGuS strings benchmark and found it competitive with
other enumerative search solvers. Moreover, Absynthe's ability to combine
abstract domains allows the user to move along a cost spectrum, i.e., expressive
domains prune more programs but require more time. Finally, to verify
Absynthe can act as a general purpose synthesis tool, we use Absynthe to
synthesize Pandas data frame manipulating programs in Python using simple
abstractions like types and column labels of a data frame. Absynthe reaches
parity with AutoPandas, a deep learning based tool for the same benchmark
suite. In summary, our results demonstrate Absynthe is a
promising step forward towards a general-purpose approach to synthesis that may
broaden the applicability of synthesis to more full-featured languages.

Tue 20 Jun

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09:00 - 11:00
PLDI: SynthesisPLDI Research Papers at Royal
Chair(s): Ilya Sergey National University of Singapore

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Trace-Guided Inductive Synthesis of Recursive Functional ProgramsDistinguished Paper
PLDI Research Papers
Yongwei Yuan Purdue University, Arjun Radhakrishna Microsoft, Roopsha Samanta Purdue University
Inductive Program Synthesis via Iterative Forward-Backward Abstract Interpretation
PLDI Research Papers
Yongho Yoon Seoul National University, Woosuk Lee Hanyang University, Kwangkeun Yi Seoul National University
ImageEye: Batch Image Processing using Program Synthesis
PLDI Research Papers
Celeste Barnaby University of Texas at Austin, Qiaochu Chen University of Texas at Austin, Roopsha Samanta Purdue University, Işıl Dillig University of Texas at Austin
One Pixel Adversarial Attacks via Sketched Programs
PLDI Research Papers
Absynthe: Abstract Interpretation-Guided Synthesis
PLDI Research Papers
Sankha Narayan Guria University of Maryland, Jeffrey S. Foster Tufts University, David Van Horn University of Maryland
DOI Pre-print
Conflict-Driven Synthesis for Layout Engines
PLDI Research Papers
Junrui Liu University of California, Santa Barbara, Yanju Chen University of California at Santa Barbara, Eric Atkinson MIT, Yu Feng University of California at Santa Barbara, Rastislav Bodík Google Research, Brain Team