Synthesizing geometry constructions

Gulwani, Sumit; Korthikanti, Vijay Anand; Tiwari, Ashish

doi:10.1145/2345156.1993505

Cited by 17 publications

(28 citation statements)

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“…Previous work in the area of automated program generation [1] relates to our work in that the high level specifications are used as the basis to derive programs. Closer to our work is that in the area of program synthesis, more specifically, that which makes use of solvers to derive a function that maps an input to an output (e.g., [9]). The key difference is that our approach uses the solver to find a match against real programs that have been encoded, while these synthesis efforts have to define a domain specific grammar that can be traversed exhaustively to generate a program that matches the programmers' constraints.…”

Section: Related Workmentioning

confidence: 99%

Solving Semantic Searches for Source Code

Stolee¹,

Elbaum²,

Dobos³

2012

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Abstract-Programmers search for code frequently utilizing syntactic queries. The effectiveness of this type of search depends on the ability of a programmer to specify a query that captures how the desired code may have been implemented, and the results often include many irrelevant matches that must be filtered manually. More semantic search approaches could address these limitations, yet the existing approaches either do not scale or require for the programmer to define complex queries. Instead, our approach to semantic search requires for the programmer to write lightweight, incomplete specifications, such as an example input and expected output of a desired function. Unlike existing approaches to semantic search, we use an SMT solver to identify programs in a repository, encoded as constraints, that match the programmer-provided specification. We instantiate the approach on subsets of the Java string library, Yahoo! Pipes mashup language, and SQL select statements, and begin to assess its effectiveness and efficiency through evaluations in each domain. I. INTRODUCTIONToday, searching for code is a regular activity for most programmers [21]. Yet, the mechanisms to support this activity have barely evolved in the last decade, and the limitations are becoming more apparent as code repositories get richer and programmers' expertise and needs more diverse.Consider a novice Java programmer who is trying to find a snippet of code that extracts an alias from an e-mail address. The programmer turns to Google (like many others [21]) and issues a search query with the following keywords: extract alias from e-mail address in Java. As expected, the query returns millions of results. None of the top ten results (a typical IR measure to assess the precision of search engine results [5]), even provide a method for decomposing an e-mail address into parts, which is the first step towards extracting the alias. Now, if the programmer is knowledgable enough about the domain to refine the query with the term substring, then the top ten results include two relevant solutions. This illustrates what occurs in practice, where programmers must sift through many irrelevant results, especially when the desired behavior cannot be tied to source code syntax or documentation.Our work targets this limitation. The general idea is that programmers provide concrete behavioral specifications as inputs and outputs and an SMT solver identifies available source code, encoded as constraints, that matches the specifications.For example, when searching for a program that extracts the alias from an e-mail address, the input could be the string "susie@mail.com" and the output the string "susie". This form of query, while more costly than a keyword query, lets the programmer specify the desired behavior, without the need to know how to achieve a certain outcome, just what that outcome is.

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Section: Related Workmentioning

confidence: 99%

Solving Semantic Searches for Source Code

Stolee¹,

Elbaum²,

Dobos³

2012

View full text Add to dashboard Cite

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“…This subfield develops software engineering technologies (e.g., software testing and analysis [7], software analytics [23], [24]) for general educational tasks, going beyond educational tasks for software engineering. For example, general educational tasks can even be on teaching maths [1], [6], [15]. As an analogy, data mining for software engineering [21] (also called mining software repositories [8]) leverages data mining technologies (which typically come from the data mining community) to address tasks in software engineering, whereas educational software engineering leverages software engineering technologies (which typically come from the software engineering community) to address tasks in education.…”

Section: Introductionmentioning

confidence: 99%

“…Educational software engineering can and will contribute significant solutions to address various critical challenges in education especially MOOCs such as automatic grading [16], [19], intelligent tutoring [12], problem generation [1], [6], [15], and plagiarism detection [10], [13].…”

Section: Introductionmentioning

confidence: 99%

“…A coding duel created by a game creator (who could be any user of Pex4Fun) consists of two methods with the same method signature and return type 6 . One of these two methods is the secret (golden) implementation, which is not visible to the player.…”

Section: Introductionmentioning

confidence: 99%

“…Example feedback is shown in the table near the bottom of Figure 1. In the table, the first line prefixed with a green circle indicates the first type of feedback, and the second and third 6 The method signature of a coding duel must have at least one input parameter. The return type of a coding duel must not be void.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Educational software engineering: Where software engineering, education, and gaming meet

Xie

Tillmann

Halleux

2013

2013 3rd International Workshop on Games and Software Engineering: Engineering Computer Games to Enable Positive, Progressive C

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Abstract-We define and advocate the subfield of educational software engineering (i.e., software engineering for education), which develops software engineering technologies (e.g., software testing and analysis, software analytics) for general educational tasks, going beyond educational tasks for software engineering. In this subfield, gaming technologies often play an important role together with software engineering technologies. We expect that researchers in educational software engineering would be among key players in the education domain and in the coming age of Massive Open Online Courses (MOOCs). Educational software engineering can and will contribute significant solutions to address various critical challenges in education especially MOOCs such as automatic grading, intelligent tutoring, problem generation, and plagiarism detection. In this position paper, we define educational software engineering and illustrate Pex for Fun (in short as Pex4Fun), one of our recent examples on leveraging software engineering and gaming technologies to address educational tasks on teaching and learning programming and software engineering skills.

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Automatically Building Diagrams for Olympiad Geometry Problems

Krueger

Han

Selsam

2021

Automated Deduction – CADE 28

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We present a method for automatically building diagrams for olympiad-level geometry problems and implement our approach in a new open-source software tool, the Geometry Model Builder (GMB). Central to our method is a new domain-specific language, the Geometry Model-Building Language (GMBL), for specifying geometry problems along with additional metadata useful for building diagrams. A GMBL program specifies (1) how to parameterize geometric objects (or sets of geometric objects) and initialize these parameterized quantities, (2) which quantities to compute directly from other quantities, and (3) additional constraints to accumulate into a (differentiable) loss function. A GMBL program induces a (usually) tractable numerical optimization problem whose solutions correspond to diagrams of the original problem statement, and that we can solve reliably using gradient descent. Of the 39 geometry problems since 2000 appearing in the International Mathematical Olympiad, 36 can be expressed in our logic and our system can produce diagrams for 94% of them on average. To the best of our knowledge, our method is the first in automated geometry diagram construction to generate models for such complex problems.

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Synthesizing geometry constructions

Cited by 17 publications

References 0 publications

Solving Semantic Searches for Source Code

Solving Semantic Searches for Source Code

Educational software engineering: Where software engineering, education, and gaming meet

Automatically Building Diagrams for Olympiad Geometry Problems

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