Program Synthesis with Equivalence Reduction

Smith, Calvin; Albarghouthi, Aws

doi:10.1007/978-3-030-11245-5_2

Cited by 20 publications

(16 citation statements)

References 34 publications

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“…Both approaches limit the synthesis search space by building their solutions from the bottom up, from a selection of base components. Smith and Albarghouthi [25] prune the search space using bottom up algebraic rewriting of the program into an equivalent normal form. In contrast to these, Sketcham derives its benefits from breaking apart input sketches from the top down, at function level granularity.…”

Section: Related Workmentioning

confidence: 99%

Program Sketching by Automatically Generating Mocks from Tests

Bragg

Foster

Roux

et al. 2021

Computer Aided Verification

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Sketch is a popular program synthesis tool that solves for unknowns in a sketch or partial program. However, while Sketch is powerful, it does not directly support modular synthesis of dependencies, potentially limiting scalability. In this paper, we introduce Sketcham, a new technique that modularizes a regular sketch by automatically generating mocks—functions that approximate the behavior of complete implementations—from the sketch’s test suite. For example, if the function f originally calls g, Sketcham creates a mock $$g_m$$ g m from g’s tests and augments the sketch with a version of f that calls $$g_m$$ g m . This change allows the unknowns in f and g to be solved separately, enabling modular synthesis with no extra work from the Sketch user. We evaluated Sketcham on ten benchmarks, performing enough runs to show at a 95% confidence level that Sketcham improves median synthesis performance on six of our ten benchmarks by a factor of up to 5$$\times $$ × compared to plain Sketch, including one benchmark that times out on Sketch, while exhibiting similar performance on the remaining four. Our results show that Sketcham can achieve modular synthesis by automatically generating mocks from tests.

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Section: Related Workmentioning

confidence: 99%

Program Sketching by Automatically Generating Mocks from Tests

Bragg

Foster

Roux

et al. 2021

Computer Aided Verification

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“…Inductive Synthesis. In the area of SyGuS [3], tractable bottom-up enumeration is commonly achieved by some form of equivalence reduction [39]. When dealing with concrete input-output examples, observational equivalence [2,42] is very effective.…”

Section: Related Workmentioning

confidence: 99%

“…Most forms of software verification and synthesis rely on some form of logical reasoning to complete their task. Whether it is checking pre-and post-conditions, deriving specifications for sub-problems [1,19], or equivalence reduction [39], these methods rely on assumptions from both the input and relevant background knowledge. Domain-specific knowledge can reinforce these methods, whether via the design of a domain-specific language [29,36,45], specialized decision procedures [28], or decomposing specifications [35].…”

Section: Introductionmentioning

confidence: 99%

Theory Exploration Powered by Deductive Synthesis

Singher

Itzhaky

2021

Computer Aided Verification

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This paper presents a symbolic method for automatic theorem generation based on deductive inference. Many software verification and reasoning tasks require proving complex logical properties; coping with this complexity is generally done by declaring and proving relevant sub-properties. This gives rise to the challenge of discovering useful sub-properties that can assist the automated proof process. This is known as the theory exploration problem, and so far, predominant solutions that emerged rely on evaluation using concrete values. This limits the applicability of these theory exploration techniques to complex programs and properties.In this work, we introduce a new symbolic technique for theory exploration, capable of (offline) generation of a library of lemmas from a base set of inductive data types and recursive definitions. Our approach introduces a new method for using abstraction to overcome the above limitations, combining it with deductive synthesis to reason about abstract values. Our implementation has shown to find more lemmas than prior art, avoiding redundant lemmas (in terms of provability), while being faster in most cases. This new abstraction-based theory exploration method is a step toward applying theory exploration to software verification and synthesis.

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“…Unlike equivalence reductions based on semantic information (e.g. [Feser et al 2015;Smith and Albarghouthi 2019]), observational equivalence makes decisions solely based on execution values, making it lightweight to maintain, requiring only the ability to execute programs. A more formal definition appears in the extended version of this paper.…”

Section: Bottom-up Synthesis With Observational Equivalencementioning

confidence: 99%

Programming with a read-eval-synth loop

Peleg

Gabay

Itzhaky

et al. 2020

Proc. ACM Program. Lang.

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A frequent programming pattern for small tasks, especially expressions, is to repeatedly evaluate the program on an input as its editing progresses. The Read-Eval-Print Loop (REPL) interaction model has been a successful model for this programming pattern. We present the new notion of Read-Eval-Synth Loop (RESL) that extends REPL by providing in-place synthesis on parts of the expression marked by the user. RESL eases programming by synthesizing parts of a required solution. The underlying synthesizer relies on a partial solution from the programmer and a few examples.RESL hinges on bottom-up synthesis with general predicates and sketching, generalizing programming by example. To make RESL practical, we present a formal framework that extends observational equivalence to non-example specifications.We evaluate RESL by conducting a controlled within-subjects user-study on 19 programmers from 8 companies, where programmers are asked to solve a small but challenging set of competitive programming problems. We find that programmers using RESL solve these problems with far less need to edit the code themselves and by browsing documentation far less. In addition, they are less likely to leave a task unfinished and more likely to be correct. CCS Concepts: • Software and its engineering → Source code generation; Automatic programming.

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Program Synthesis with Equivalence Reduction

Cited by 20 publications

References 34 publications

Program Sketching by Automatically Generating Mocks from Tests

Program Sketching by Automatically Generating Mocks from Tests

Theory Exploration Powered by Deductive Synthesis

Programming with a read-eval-synth loop

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