Look for the Proof to Find the Program: Decorated-Component-Based Program Synthesis

Gascón, Adrià; Tiwari, Ashish; Carmer, Brent; Mathur, Umang

doi:10.1007/978-3-319-63390-9_5

Cited by 9 publications

(7 citation statements)

References 33 publications

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“…In component-based synthesis, a small user-provided set of components are combined to synthesize the target program. Similar to Sketch [Solar-Lezama et al 2006], Gascón et al [2017]; Jha et al [2010] have the end-user place a syntactic bias on the search space by specifying the set of components needed. Feng et al [2017] have the developer of the synthesis engine instantiate the engine with a set of components, similar to Gauss.…”

Section: Related Workmentioning

confidence: 99%

Gauss: program synthesis by reasoning over graphs

Bavishi

Lemieux

Sen

et al. 2021

Proc. ACM Program. Lang.

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While input-output examples are a natural form of specification for program synthesis engines, they can be imprecise for domains such as table transformations. In this paper, we investigate how extracting readily-available information about the user intent behind these input-output examples helps speed up synthesis and reduce overfitting. We present Gauss, a synthesis algorithm for table transformations that accepts partial input-output examples, along with user intent graphs. Gauss includes a novel conflict-resolution reasoning algorithm over graphs that enables it to learn from mistakes made during the search and use that knowledge to explore the space of programs even faster. It also ensures the final program is consistent with the user intent specification, reducing overfitting. We implement Gauss for the domain of table transformations (supporting Pandas and R), and compare it to three state-of-the-art synthesizers accepting only input-output examples. We find that it is able to reduce the search space by 56×, 73× and 664× on average, resulting in 7×, 26× and 7× speedups in synthesis times on average, respectively.

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Section: Related Workmentioning

confidence: 99%

Gauss: program synthesis by reasoning over graphs

Bavishi

Lemieux

Sen

et al. 2021

Proc. ACM Program. Lang.

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“…In particular, synthesis algorithms that use specifications can be grouped along two axes: (a) whether they require exact vs. approximate specifications, and (b) whether they use specifications to guide search or completely reduce synthesis to constraint solving. There are several techniques that formulate synthesis as a constraint solving problem [12,14,16,34]. For example, Brahma [16] uses component specifications to generate an ∃∀ formula such that any satisfying assignment to this formula is a solution to the synthesis problem.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Brahma [16] uses component specifications to generate an ∃∀ formula such that any satisfying assignment to this formula is a solution to the synthesis problem. More recent work such as Synudic [12] also reduces synthesis to constraint solving, but uses the abstract semantics of components to simplify the resulting constraint-solving problem.…”

Section: Related Workmentioning

confidence: 99%

Program synthesis using conflict-driven learning

Martins

Bastani

et al. 2018

Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation

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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 generalpurpose 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 conflictdriven learning and show that Neo outperforms two stateof-the-art synthesis tools, Morpheus and DeepCoder, that target these respective domains. CCS Concepts • Software and its engineering → Programming by example; Automatic programming;

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“…There is a line of work that uses abstractions in the context of component-based program synthesis [Gascón et al 2017;Tiwari et al 2015]. These techniques annotate each component with a "decoration" that serves as an abstraction of the semantics of that component.…”

Section: Related Workmentioning

confidence: 99%

Program synthesis using abstraction refinement

Wang

Dillig

Singh

2017

Proc. ACM Program. Lang.

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We present a new approach to example-guided program synthesis based on counterexample-guided abstraction refinement. Our method uses the abstract semantics of the underlying DSL to find a program P whose abstract behavior satisfies the examples. However, since program P may be spurious with respect to the concrete semantics, our approach iteratively refines the abstraction until we either find a program that satisfies the examples or prove that no such DSL program exists. Because many programs have the same input-output behavior in terms of their abstract semantics, this synthesis methodology significantly reduces the search space compared to existing techniques that use purely concrete semantics.While synthesis using abstraction refinement (SYNGAR) could be implemented in different settings, we propose a refinement-based synthesis algorithm that uses abstract finite tree automata (AFTA). Our technique uses a coarse initial program abstraction to construct an initial AFTA, which is iteratively refined by constructing a proof of incorrectness of any spurious program. In addition to ruling out the spurious program accepted by the previous AFTA, proofs of incorrectness are also useful for ruling out many other spurious programs.We implement these ideas in a framework called Blaze, which can be instantiated in different domains by providing a suitable DSL and its corresponding concrete and abstract semantics. We have used the Blaze framework to build synthesizers for string and matrix transformations, and we compare Blaze with existing techniques. Our results for the string domain show that Blaze compares favorably with FlashFill, a domainspecific synthesizer that is now deployed in Microsoft PowerShell. In the context of matrix manipulations, we compare Blaze against Prose, a state-of-the-art general-purpose VSA-based synthesizer, and show that Blaze results in a 90x speed-up over Prose. In both application domains, Blaze also consistently improves upon the performance of two other existing techniques by at least an order of magnitude. Abstract Synthesizer Checker Refiner candidate program Failure (no solution) Synthesized program counterexample & spurious program new abstraction End-users Domain expert Examples DSL w/ abstract semantics SYNGAR It can be shown that the language of the CFTA constructed from Fig. 3 is exactly the set of abstract syntax trees (ASTs) of DSL programs that are consistent with the input-output examples. 1 Hence, once we construct such a CFTA, the synthesis task boils down to finding an AST that is accepted by the automaton. However, since there are typically many ASTs accepted by the CFTA, one can use heuristics to identify the "best" program that satisfies the input-output examples. 1 The proof can be found in the extended version of this paper [Wang et al. 2017a].AFTA constr uction AST annotated with old abstract values AST annotated with concrete values Pr oof constr uction Rank Predicates Predicates 2 AST annotated with old abstract values AST annotated with concrete values Pr oof co...

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Look for the Proof to Find the Program: Decorated-Component-Based Program Synthesis

Cited by 9 publications

References 33 publications

Gauss: program synthesis by reasoning over graphs

Gauss: program synthesis by reasoning over graphs

Program synthesis using conflict-driven learning

Program synthesis using abstraction refinement

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