Constraint-based type-directed program synthesis

Osera, Peter-Michael

doi:10.1145/3331554.3342608

Cited by 13 publications

(5 citation statements)

References 19 publications

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“…We also relate to work on program synthesis (Solar-Lezama 2009; Jha et al 2010;Feng et al 2017;Osera 2019) where the goal is to produce a valid program for a given set of constraints. Here, the output of a program is designed to meet a given specification.…”

Section: Related Workmentioning

confidence: 99%

MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks

Hoernle

Karampatsis

Belle

et al. 2022

AAAI

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We propose a novel way to incorporate expert knowledge into the training of deep neural networks. Many approaches encode domain constraints directly into the network architecture, requiring non-trivial or domain-specific engineering. In contrast, our approach, called MultiplexNet, represents domain knowledge as a quantifier-free logical formula in disjunctive normal form (DNF) which is easy to encode and to elicit from human experts. It introduces a latent Categorical variable that learns to choose which constraint term optimizes the error function of the network and it compiles the constraints directly into the output of existing learning algorithms. We demonstrate the efficacy of this approach empirically on several classical deep learning tasks, such as density estimation and classification in both supervised and unsupervised settings where prior knowledge about the domains was expressed as logical constraints. Our results show that the MultiplexNet approach learned to approximate unknown distributions well, often requiring fewer data samples than the alternative approaches. In some cases, MultiplexNet finds better solutions than the baselines; or solutions that could not be achieved with the alternative approaches. Our contribution is in encoding domain knowledge in a way that facilitates inference. We specifically focus on quantifier-free logical formulae that are specified over the output domain of a network. We show that this approach is both efficient and general; and critically, our approach guarantees 100% constraint satisfaction in a network's output.

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Section: Related Workmentioning

confidence: 99%

MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks

Hoernle

Karampatsis

Belle

et al. 2022

AAAI

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“…Types. Annotated types signatures or hints are often used to direct program synthesis, most commonly for functional programs [37,38]. Myth [39] uses type signatures alongside examples to synthesize recursive functional programs, while [45] uses refinement types to guide the search process [45].…”

Section: Related Workmentioning

confidence: 99%

Modeling black-box components with probabilistic synthesis

Collie

Woodruff

O’Boyle

2020

Proceedings of the 19th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences

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This paper is concerned with synthesizing programs based on black-box oracles: we are interested in the case where there exists an executable implementation of a component or library, but its internal structure is unknown. We are provided with just an API or function signature, and aim to synthesize a program with equivalent behavior.To attack this problem, we detail Presyn: a program synthesizer designed for flexible interoperation with existing programs and compiler toolchains. Presyn uses high-level imperative control-flow structures and a pair of cooperating predictive models to efficiently narrow the space of potential programs. These models can be trained effectively on small corpora of synthesized examples.We evaluate Presyn against five leading program synthesizers on a collection of 112 synthesis benchmarks collated from previous studies and real-world software libraries. We show that Presyn is able to synthesize a wider range of programs than each of them with less human input. We demonstrate the application of our approach to real-world code and software engineering problems with two case studies: accelerator library porting and detection of duplicated library reimplementations.CCS Concepts: • Software and its engineering → General programming languages; Automatic programming; Programming by example; Genetic programming.

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“…Simpl overcomes this problem by assuming a partial program is already provided (such as a loop structure) [47]. Other work aims to complete suggested sketches [49] of programs to provide programmer abstraction and auto-parallelization [18] Type signatures and information are often used to direct program synthesis, most commonly for functional programs [34,35]. Other work uses extended type information as a means of accessing heterogeneous accelerators [11].…”

Section: Program Synthesismentioning

confidence: 99%

M3: Semantic API Migrations

Collie,

Ginsbach,

Woodruff

et al. 2020

Preprint

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Library migration is a challenging problem, where most existing approaches rely on prior knowledge. This can be, for example, information derived from changelogs or statistical models of API usage.This paper addresses a different API migration scenario where there is no prior knowledge of the target library. We have no historical changelogs and no access to its internal representation. To tackle this problem, this paper proposes a novel approach (M 3 ), where probabilistic program synthesis is used to semantically model the behavior of library functions. Then, we use an SMT-based code search engine to discover similar code in user applications. These discovered instances provide potential locations for API migrations.We evaluate our approach against 7 well-known libraries from varied application domains, learning correct implementations for 94 functions. Our approach is integrated with standard compiler tooling, and we use this integration to evaluate migration opportunities in 9 existing C/C++ applications with over 1MLoC. We discover over 7,000 instances of these functions, of which more than 2,000 represent migration opportunities. CCS CONCEPTS• Software and its engineering → Software maintenance tools; Software verification and validation; Compilers.

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Constraint-based type-directed program synthesis

Cited by 13 publications

References 19 publications

MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks

MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks

Modeling black-box components with probabilistic synthesis

M3: Semantic API Migrations

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