Spreadsheet data manipulation using examples

Gulwani, Sumit; Harris, William R.; Singh, Rishabh

doi:10.1145/2240236.2240260

Cited by 238 publications

(196 citation statements)

References 21 publications

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“…We call such an approach to synthesis syntax-guided synthesis (SyGuS). Besides program sketching, a number of recent efforts such as synthesis of loop-free programs [5], synthesis of Excel macros from examples [6], program de-obfuscation [7], synthesis of protocols from the skeleton and example behaviors [8], synthesis of loop-bodies from pre/post conditions [9], integration of constraint solvers in programming environments for program completion [10], and super-optimization by finding equivalent shorter loop bodies [11], all are arguably instances of syntaxguided synthesis. Also related are techniques for automatic generation of invariants using templates and by learning [12]- [14], and recent work on solving quantified Horn clauses [15].…”

Section: Introductionmentioning

confidence: 99%

Syntax-guided synthesis

Alur

Bodík

Juniwal

et al. 2013

2013 Formal Methods in Computer-Aided Design

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478

528

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Abstract-The classical formulation of the program-synthesis problem is to find a program that meets a correctness specification given as a logical formula. Recent work on program synthesis and program optimization illustrates many potential benefits of allowing the user to supplement the logical specification with a syntactic template that constrains the space of allowed implementations. Our goal is to identify the core computational problem common to these proposals in a logical framework. The input to the syntax-guided synthesis problem (SyGuS) consists of a background theory, a semantic correctness specification for the desired program given by a logical formula, and a syntactic set of candidate implementations given by a grammar. The computational problem then is to find an implementation from the set of candidate expressions so that it satisfies the specification in the given theory. We describe three different instantiations of the counter-example-guided-inductive-synthesis (CEGIS) strategy for solving the synthesis problem, report on prototype implementations, and present experimental results on an initial set of benchmarks.

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Section: Introductionmentioning

confidence: 99%

Syntax-guided synthesis

Alur

Bodík

Juniwal

et al. 2013

2013 Formal Methods in Computer-Aided Design

Self Cite

478

528

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show abstract

“…However, 99% of these end users do not have programming expertise and they often struggle with repetitive tasks in various domains that could otherwise be automated using small scripts. Programming-by-examples (PBE) [19,5] has the potential to revolutionize this landscape since users can often specify their intent using examples as has been observed on various help forums [8]. PBE involves techniques that generalize example behaviors on concrete inputs provided by the user into programs that can operate on new unseen inputs.…”

Section: Introductionmentioning

confidence: 99%

User Interaction Models for Disambiguation in Programming by Example

Mayer

Soares

Grechkin

et al. 2015

Proceedings of the 28th Annual ACM Symposium on User Interface Software &Amp; Technology

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Programming by Examples (PBE) has the potential to revolutionize end-user programming by enabling end users, most of whom are non-programmers, to create small scripts for automating repetitive tasks. However, examples, though often easy to provide, are an ambiguous specification of the user's intent. Because of that, a key impedance in adoption of PBE systems is the lack of user confidence in the correctness of the program that was synthesized by the system. We present two novel user interaction models that communicate actionable information to the user to help resolve ambiguity in the examples. One of these models allows the user to effectively navigate between the huge set of programs that are consistent with the examples provided by the user. The other model uses active learning to ask directed example-based questions to the user on the test input data over which the user intends to run the synthesized program. Our user studies show that each of these models significantly reduces the number of errors in the performed task without any difference in completion time. Moreover, both models are perceived as useful, and the proactive active-learning based model has a slightly higher preference regarding the users' confidence in the result.

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“…Code synthesis traditionally attempts to start from an empty or near-empty program file, either to produce a program which matches a desired input-output mapping [19], or which approximates a function (such as a regressor or classifier) [20].…”

Section: Related Workmentioning

confidence: 99%

“…Gulwani et al propose an approach to generate a computer program that transforms a set of input examples to a set of corresponding output examples [19]. This is done by first defining a domain-specific language for a certain class of program (like string manipulation) which helps to restrict the search space for synthesising a program.…”

Section: Related Workmentioning

confidence: 99%

Experiments in genetic divergence for emergent systems

McGowan

Wild

Porter

2018

Proceedings of the 4th International Workshop on Genetic Improvement Workshop

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Emergent software systems take a step towards tackling the everincreasing complexity of modern software, by having systems selfassemble from a library of building blocks, and then continually re-assemble themselves from alternative building blocks to learn which compositions of behaviour work best in each deployment environment. One of the key challenges in emergent systems is populating the library of building blocks, and particularly a set of alternative implementations of particular building blocks, which form the runtime search space of optimal behaviour. We present initial work in using a fusion of genetic improvement and genetic synthesis to automatically populate a divergent set of implementations of the same functionality, allowing emergent systems to explore new behavioural alternatives without human input. Our early results indicate this approach is able to successfully yield useful divergent implementations of building blocks which are more suited than any existing alternative for particular operating conditions.

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Spreadsheet data manipulation using examples

Cited by 238 publications

References 21 publications

Syntax-guided synthesis

Syntax-guided synthesis

User Interaction Models for Disambiguation in Programming by Example

Experiments in genetic divergence for emergent systems

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