Sketching stencils

Solar-Lezama, Armando; Arnold, Gilad; Tancau, Liviu; Bodík, Rastislav; Saraswat, Vijay; Seshia, Sanjit A.

doi:10.1145/1273442.1250754

Cited by 46 publications

(38 citation statements)

References 23 publications

(32 reference statements)

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“…Some of these approaches also utilize supplementary information like the code structure to help guide the solver (Singh and Solar-Lezama, 2011). Another means for program synthesis utilizes partial programs called sketches (e.g., Raabe and Bodik, 2009;Solar-Lezama et al, 2007. These approaches typically operate over small, low-level and finite programs like bit vector manipulation (Solar-Lezama et al, 2006) and hardware circuits (Raabe and Bodik, 2009), but more recently have targeted higherlevel applications such as concurrent data structures (Solar-Lezama et al, 2008) and optimizing code (Solar-Lezama et al, 2007).…”

Section: Related Workmentioning

confidence: 99%

“…Another means for program synthesis utilizes partial programs called sketches (e.g., Raabe and Bodik, 2009;Solar-Lezama et al, 2007. These approaches typically operate over small, low-level and finite programs like bit vector manipulation (Solar-Lezama et al, 2006) and hardware circuits (Raabe and Bodik, 2009), but more recently have targeted higherlevel applications such as concurrent data structures (Solar-Lezama et al, 2008) and optimizing code (Solar-Lezama et al, 2007). Recent efforts add context from a program execution using breakpoints and suggests snippets of code to integrate into a code base (Galenson et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

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Code search with input/output queries: Generalizing, ranking, and assessment

Stolee

Elbaum

Dwyer

2016

Journal of Systems and Software

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a b s t r a c tIn this work we generalize, improve, and extensively assess our semantic source code search engine through which developers use an input/output query model to specify what behavior they want instead of how it may be implemented. Under this approach a code repository contains programs encoded as constraints and an SMT solver finds encoded programs that match an input/output query. The search engine returns a list of source code snippets that match the specification.The initial instantiation of this approach showed potential but was limited. It only encoded single-path programs, reported just complete matches, did not rank the results, and was only partly assessed. In this work, we explore the use of symbolic execution to address some of these technical shortcomings. We implemented a tool, Satsy, that uses symbolic execution to encode multi-path programs as constraints and a novel ranking algorithm based on the strength of the match between an input/output query and the program paths traversed by symbolic execution. An assessment about the relevance of Satsy's results versus other search engines, Merobase and Google, on eight novice-level programming tasks gathered from StackOverflow, using the opinions of 30 study participants, reveals that Satsy often out-performs the competition in terms of precision, and that matches are found in seconds.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Code search with input/output queries: Generalizing, ranking, and assessment

Stolee

Elbaum

Dwyer

2016

Journal of Systems and Software

View full text Add to dashboard Cite

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“…When program sketching, a programmer needs to write only a skeleton, or sketch, of a desired implementation, leaving holes that a synthesizer fills in [27][28][29][30]. Prorogued calls can be seen as these holes.…”

Section: Related Workmentioning

confidence: 99%

Liberating the programmer with prorogued programming

Afshari

Barr

2012

Proceedings of the ACM International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software

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Programming is the process of expressing and refining ideas in a programming language. Ideally, we want our programming language to flexibly fit our natural thought process. Language innovations, such as procedural abstraction, object and aspect orientation, have helped increase programming agility. However, they still lack important features that a programmer could exploit to quickly experiment with design and implementation choices.We propose prorogued programming, a new paradigm more closely aligned with a programmer's thought process. A prorogued programming language (PPL) supports three basic principles: 1) proroguing concerns 1 : the ability to defer a concern, to focus on and finish the current concern; 2) hybrid computation: the ability to involve the programmer as an integral part of computation; and 3) executable refinement: the ability to execute any intermediate program refinements. Working in a PPL, the programmer can run and experiment with an incomplete program, and gradually and iteratively reify the missing parts while catching design and implementation mistakes early. We describe the prorogued programming paradigm, our design and realization of the paradigm using Prorogued C#, our extension to C#, and demonstrate its utility through a few use cases.

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“…Program sketching [21] may be applied to synthesize programs from examples. In program sketching, a programmer supplies to a sketcher an incomplete version of a program (a sketch) and a specification of correct behavior for the program, possibly as a set of input-output examples.…”

Section: Related Workmentioning

confidence: 99%

Spreadsheet table transformations from examples

Harris

Gulwani

2011

Proceedings of the 32nd ACM SIGPLAN Conference on Programming Language Design and Implementation

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Every day, millions of computer end-users need to perform tasks over large, tabular data, yet lack the programming knowledge to do such tasks automatically. In this work, we present an automatic technique that takes from a user an example of how the user needs to transform a table of data, and provides to the user a program that implements the transformation described by the example. In particular, we present a language of programs TableProg that can describe transformations that real users require. We then present an algorithm ProgFromEx that takes an example input and output table, and infers a program in TableProg that implements the transformation described by the example. When the program is applied to the example input, it reproduces the example output. When the program is applied to another, potentially larger, table with a "similar" layout as the example input table, then the program produces a corresponding table with a layout that is similar to the example output table. A user can apply ProgFromEx interactively, providing multiple small examples to obtain a program that implements the transformation that the user desires. Moreover, ProgFromEx can help identify "noisy" examples that contain errors.To evaluate the practicality of TableProg and ProgFromEx, we implemented ProgFromEx as a module for the Microsoft Excel spreadsheet program. We applied the module to automatically implement over 50 table transformations specified by endusers through examples on online Excel help forums. In seconds, ProgFromEx found programs that satisfied the examples and could be applied to larger input tables. This experience demonstrates that TableProg and ProgFromEx can significantly automate the tasks over tabular data that users need to perform.

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Sketching stencils

Cited by 46 publications

References 23 publications

Code search with input/output queries: Generalizing, ranking, and assessment

Code search with input/output queries: Generalizing, ranking, and assessment

Liberating the programmer with prorogued programming

Spreadsheet table transformations from examples

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