A simple inductive synthesis methodology and its applications

Itzhaky, Shachar; Gulwani, Sumit; Immerman, Neil; Sagiv, Mooly

doi:10.1145/1869459.1869463

Cited by 37 publications

(29 citation statements)

References 10 publications

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“…Construct a triangle given one angle, the side opposite this angle, and the length of altitude to that side. 16. Construct a that is inscribed in a given quadrant of a .…”

Section: Goal-directed Search: Goodness Measurementioning

confidence: 99%

“…Program Synthesis using Input-Output Examples There is a lot of work on inductive program synthesis where the specification from the user comes in the form of multiple input-output examples: string manipulation macros [13], table manipulation programs [15], bit-vector algorithms [18], graph algorithms [16]. In contrast, our system accepts logical specifications, but for scalability reasons, internally transforms the logical specification into a (probabilistically equivalent) input-output example based specification.…”

Section: Related Workmentioning

confidence: 99%

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Synthesizing geometry constructions

2011

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In this paper, we study the problem of automatically solving ruler/compass based geometry construction problems. We first introduce a logic and a programming language for describing such constructions and then phrase the automation problem as a program synthesis problem. We then describe a new program synthesis technique based on three key insights: (i) reduction of symbolic reasoning to concrete reasoning (based on a deep theoretical result that reduces verification to random testing), (ii) extending the instruction set of the programming language with higher level primitives (representing basic constructions found in textbook chapters, inspired by how humans use their experience and knowledge gained from chapters to perform complicated constructions), and (iii) pruning the forward exhaustive search using a goal-directed heuristic (simulating backward reasoning performed by humans). Our tool can successfully synthesize constructions for various geometry problems picked up from high-school textbooks and examination papers in a reasonable amount of time. This opens up an amazing set of possibilities in the context of making classroom teaching interactive.

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“…Construct a triangle given one angle, the side opposite this angle, and the length of altitude to that side. 16. Construct a that is inscribed in a given quadrant of a .…”

Section: Goal-directed Search: Goodness Measurementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Synthesizing geometry constructions

2011

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“…Elixir uses their worklists for dynamic scheduling, and adds static scheduling and synthesis from a high-level specification of operators. Another line of work focuses on synthesis from logic specifications [17,33]. The user writes a logical formula and a system synthesizes a program from that.…”

Section: Related Workmentioning

confidence: 99%

Elixir

2012

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Algorithms in new application areas like machine learning and network analysis use "irregular" data structures such as graphs, trees and sets. Writing efficient parallel code in these problem domains is very challenging because it requires the programmer to make many choices: a given problem can usually be solved by several algorithms, each algorithm may have many implementations, and the best choice of algorithm and implementation can depend not only on the characteristics of the parallel platform but also on properties of the input data such as the structure of the graph.One solution is to permit the application programmer to experiment with different algorithms and implementations without writing every variant from scratch. Auto-tuning to find the best variant is a more ambitious solution. These solutions require a system for automatically producing efficient parallel implementations from high-level specifications. Elixir, the system described in this paper, is the first step towards this ambitious goal. Application programmers write specifications that consist of an operator, which describes the computations to be performed, and a schedule for performing these computations. Elixir uses sophisticated inference techniques to produce efficient parallel code from such specifications.We used Elixir to automatically generate many parallel implementations for three irregular problems: breadthfirst search, single source shortest path, and betweennesscentrality computation. Our experiments show that the best generated variants can be competitive with handwritten code for these problems from other research groups; for some inputs, they even outperform the handwritten versions.

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“…Program synthesis has traditionally been motivated by the need to synthesize non-trivial algorithms [12,22,23] or to discover tricky code-snippets [10,24]. In this paper, we apply program synthesis to discovering relatively simpler programs, but those that are cared about by a much larger class of spreadsheet end-users, who often struggle with manipulating tabular data in spreadsheets.…”

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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A simple inductive synthesis methodology and its applications

Cited by 37 publications

References 10 publications

Synthesizing geometry constructions

Synthesizing geometry constructions

Elixir

Spreadsheet table transformations from examples

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