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检索条件"主题词=PROGRAMMING BY EXAMPLE"

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example-Guided Synthesis of Relational Queries 2021

Example-Guided Synthesis of Relational Queries

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42nd ACM SIGPLAN International Conference on programming Language Design and Implementation (PLDI)

作者： Thakkar, Aalok Naik, Aaditya Sands, Nathaniel Alur, Rajeev Naik, Mayur Raghothaman, Mukund Univ Penn Philadelphia PA 19104 USA Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781450383912

Program synthesis tasks are commonly specified via input-output examples. Existing enumerative techniques for such tasks are primarily guided by program syntax and only make indirect use of the examples. We identify a class of synthesis algorithms for programming-by-examples, which we call example-Guided Synthesis (EGS), that exploits latent structure in the provided examples while generating candidate programs. We present an instance of EGS for the synthesis of relational queries and evaluate it on 86 tasks from three application domains: knowledge discovery, program analysis, and database querying. Our evaluation shows that EGS outperforms state-of-the-art synthesizers based on enumerative search, constraint solving, and hybrid techniques in terms of synthesis time, quality of synthesized programs, and ability to prove unrealizability.

关键词： programming by example example-Guided Synthesis

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Maximizing Correctness with Minimal User Effort to Learn Data Transformations 16

Maximizing Correctness with Minimal User Effort to Learn Dat...

引用

21st International Conference on Intelligent User Interfaces (IUI)

作者： Wu, Bo Knoblock, Craig A. Univ South Calif Dept Comp Sci 4676 Admiralty Way Marina Del Rey CA 90007 USA Univ South Calif Inst Informat Sci 4676 Admiralty Way Marina Del Rey CA USA

ISBN: (纸本)9781450341370

Data transformation often requires users to write many trivial and task-dependent programs to transform thousands of records. Recently, programming-by-example (PBE) approaches enable users to transform data without coding. A key challenge of these PBE approaches is to deliver correctly transformed results on large datasets, since these transformation programs are likely to be generated by non-expert users. To address this challenge, existing approaches aim to identify a small set of potentially incorrect records and ask users to examine these records instead of the entire dataset. However, because the transformation scenarios are highly task-dependent, existing approaches cannot capture the incorrect records for various scenarios. We present a approach that learns from past transformation scenarios to generate a meta-classifier to identify the incorrect records. Our approach color-codes these transformed records and then presents them for users to examine. The method allows users to either enter an example for a record transformed incorrectly or confirm the correctness of a transformed record. Our approach also can learn from the users' labels to refine the meta-classifier to accurately identify the incorrect records. Simulation results and a user study show that our method can identify the incorrectly transformed records and reduce the user efforts in examining the results.

关键词： programming by example Data Transformation Program Synthesis

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Learning Text Patterns Using Separate-and-Conquer Genetic programming 18

Learning Text Patterns Using Separate-and-Conquer Genetic Pr...

引用

18th European Conference on Genetic programming (EuroGP)

作者： Bartoli, Alberto De Lorenzo, Andrea Medvet, Eric Tarlao, Fabiano Univ Trieste DIA Trieste Italy

ISBN: (纸本)9783319165011;9783319165004

The problem of extracting knowledge from large volumes of unstructured textual information has become increasingly important. We consider the problem of extracting text slices that adhere to a syntactic pattern and propose an approach capable of generating the desired pattern automatically, from a few annotated examples. Our approach is based on Genetic programming and generates extraction patterns in the form of regular expressions that may be input to existing engines without any post-processing. Key feature of our proposal is its ability of discovering automatically whether the extraction task may be solved by a single pattern, or rather a set of multiple patterns is required. We obtain this property by means of a separate-and-conquer strategy: once a candidate pattern provides adequate performance on a subset of the examples, the pattern is inserted into the set of final solutions and the evolutionary search continues on a smaller set of examples including only those not yet solved adequately. Our proposal outperforms an earlier state-of-the-art approach on three challenging datasets.

关键词： Regular expressions Multiple pattern programming by example Text extraction

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Synthesizing Data Structure Transformations from Input-Output examples 15

Synthesizing Data Structure Transformations from Input-Outpu...

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36th ACM SIGPLAN Conference on programming Language Design and Implementation

作者： Feser, John K. Chaudhuri, Swarat Dillig, Isil Rice Univ Houston TX 77251 USA UT Austin Austin TX 78712 USA

ISBN: (纸本)9781450334686

We present a method for example-guided synthesis of functional programs over recursive data structures. Given a set of input-output examples, our method synthesizes a program in a functional language with higher-order combinators like map and fold. The synthesized program is guaranteed to be the simplest program in the language to fit the examples. Our approach combines three technical ideas: inductive generalization, deduction, and enumerative search. First, we generalize the input-output examples into hypotheses about the structure of the target program. For each hypothesis, we use deduction to infer new input/output examples for the missing subexpressions. This leads to a new subproblem where the goal is to synthesize expressions within each hypothesis. Since not every hypothesis can be realized into a program that fits the examples, we use a combination of best-first enumeration and deduction to search for a hypothesis that meets our needs. We have implemented our method in a tool called lambda(2), and we evaluate this tool on a large set of synthesis problems involving lists, trees, and nested data structures. The experiments demonstrate the scalability and broad scope of lambda(2). A highlight is the synthesis of a program believed to be the world's earliest functional pearl.

关键词： Program synthesis programming by example data transformations search-based synthesis automated deduction

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TRANSREGEX: Multi-modal Regular Expression Synthesis by Generate-and-Repair 21

TRANSREGEX: Multi-modal Regular Expression Synthesis by Gene...

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43rd IEEE/ACM International Conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP) / 43rd ACM/IEEE International Conference on Software Engineering - New Ideas and Emerging Results (ICSE-NIER)

作者： Li, Yeting Li, Shuaimin Xu, Zhiwu Cao, Jialun Chen, Zixuan Hu, Yun Chen, Haiming Cheung, Shing-Chi Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Chinese Acad Sci Sci & Technol Integrated Infomat Syst Lab Inst Software Beijing Peoples R China

ISBN: (纸本)9780738113197

Since regular expressions (abbrev. regexes) are difficult to understand and compose. automatically generating regexes has been an important research problem. This paper introduces TRANSREGEX, for automatically constructing regexes from both natural language descriptions and examples. To the best of our knowledge, TRANSREGEX is the first to treat the NI.P-and-example-based regex synthesis problem as the problem of NI.P-based synthesis with regex repair. For this purpose. we present novel algorithms for both NI.P-based synthesis and regex repair. We evaluate TRANSREGEX with ten relevant state-of-the-art tools on three publicly available datasets. The evaluation results demonstrate that the accuracy of our TRANSREGEX is 17.444, 35.8% and 38.9% higher than that of NI.P-based approaches on the three datasets, respectively. Furthermore, TRANSREGEX can achieve higher accuracy than the state-of-the-art multi-modal techniques with 10% to 39% higher accuracy on all three datasets. The evaluation results also indicate TRANSREGEX utilizing natural language and examples in a more effective way.

关键词： regex synthesis regex repair programming by natural languages programming by example

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NoFAQ: Synthesizing Command Repairs from examples 2017

NoFAQ: Synthesizing Command Repairs from Examples

引用

11th Joint Meeting of European Software Engineering Conference (ESEC) / ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE)

作者： D'Antoni, Loris Singh, Rishabh Vaughn, Michael Univ Wisconsin Madison WI 53706 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781450351058

Command-line tools are confusing and hard to use due to their cryptic error messages and lack of documentation. Novice users often resort to online help-forums for finding corrections to their buggy commands, but have a hard time in searching precisely for posts that are relevant to their problem and then applying the suggested solutions to their buggy command. We present NoFAQ, a tool that uses a set of rules to suggest possible fixes when users write buggy commands that trigger commonly occurring errors. The rules are expressed in a language called FIXIT and each rule pattern-matches against the user's buggy command and corresponding error message, and uses these inputs to produce a possible fixed command. NoFAQ automatically learns FIXIT rules from examples of buggy and repaired commands. We evaluate NoFAQ on two fronts. First, we use 92 benchmark problems drawn from an existing tool and show that NoFAQ is able to synthesize rules for 81 benchmark problems in real time using just 2 to 5 input-output examples for each rule. Second, we run our learning algorithm on the examples obtained through a crowd-sourcing interface and show that the learning algorithm scales to large sets of examples.

关键词： Domain Specific Languages programming by example Program Synthesis Program Repair Command Line Interface

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CREATING GRAPHICAL INTERACTIVE APPLICATION OBJECTS BY DEMONSTRATION 89

CREATING GRAPHICAL INTERACTIVE APPLICATION OBJECTS BY DEMONS...

引用

2ND ANNUAL SYMP ON USER INTERFACE SOFTWARE AND TECHNOLOGY

作者： MYERS, BA VANDERZANDEN, B DANNENBERG, RB School of Computer Science Carnegie Mellon University Pittsburgh PA

ISBN: (纸本)0897913353

The Lapidary user interface tool allows all pictorial aspects of programs to be specified graphically. In addition, the behavior of these objects at run-time can be specified using dialogue boxes and by demonstration. In particular, Lapidary allows the designer to draw pictures of application-specific graphical objects which will be created and maintained at run-time by the application. This includes the graphical entities that the end user will manipulate (such as the components of the picture), the feedback that shows which objects are selected (such as small boxes on the sides and corners of an object), and the dynamic feedback objects (such as hair-line boxes to show where an object is being dragged). In addition, Lapidary supports the construction and use of “widgets” (sometimes called interaction techniques or gadgets) such as menus, scroll bars, buttons and icons. Lapidary therefore supports using a pre-defined library of widgets, and defining a new library with a unique “look and feel.” The run-time behavior of all these objects can be specified in a straightforward way using constraints and abstract descriptions of the interactive response to the input devices. Lapidary generalizes from the specific example pictures to allow the graphics and behaviors to be specified by demonstration.

关键词： Direct manipulation Interaction Interaction techniques Object-oriented design programming by example User interface management systems

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Spreadsheet Table Transformations from examples 11

Spreadsheet Table Transformations from Examples

引用

32nd ACM SIGPLAN Conference on programming Language Design and Implementation (PLDI 11)

作者： Harris, William R. Gulwani, Sumit Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

ISBN: (纸本)9781450306638

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 ProgFrornEx 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 ProgFrornEx 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 end-users 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.

关键词： Program Synthesis End-user programming programming by example Spreadsheet programming Table Manipulation User Intent

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Web Question Answering with Neurosymbolic Program Synthesis 2021

Web Question Answering with Neurosymbolic Program Synthesis

引用

42nd ACM SIGPLAN International Conference on programming Language Design and Implementation (PLDI)

作者： Chen, Qiaochu Lamoreaux, Aaron Wang, Xinyu Durrett, Greg Bastani, Osbert Dillig, Isil Univ Texas Austin Austin TX 78712 USA Univ Michigan Ann Arbor MI 48109 USA Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9781450383912

In this paper, we propose a new technique based on program synthesis for extracting information from webpages. Given a natural language query and a few labeled webpages, our method synthesizes a program that can be used to extract similar types of information from other unlabeled webpages. To handle websites with diverse structure, our approach employs a neurosymbolic DSL that incorporates both neural NLP models as well as standard language constructs for tree navigation and string manipulation. We also propose an optimal synthesis algorithm that generates all DSL programs that achieve optimal F-1 score on the training examples. Our synthesis technique is compositional, prunes the search space by exploiting a monotonicity property of the DSL, and uses transductive learning to select programs with good generalization power. We have implemented these ideas in a new tool called WEBQA and evaluate it on 25 different tasks across multiple domains. Our experiments show that WEBQA significantly outperforms existing tools such as state-of-the-art question answering models and wrapper induction systems.

关键词： Program Synthesis programming by example Web Information Extraction

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Synthesis From examples: Interaction Models and Algorithms (Invited Talk Paper)

Synthesis From Examples: Interaction Models and Algorithms (...

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14th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Gulwani, Sumit Microsoft Res Redmond WA 98052 USA

ISBN: (纸本)9780769549347;9781467350266

examples are often a natural way to specify various computational artifacts such as programs, queries, and sequences. Synthesizing such artifacts from example based specifications has various applications in the domains of enduser programming and intelligent tutoring systems. Synthesis from examples involves addressing two key technical challenges: (i) design of a user interaction model to deal with the inherent ambiguity in the example based specification. (ii) design of an efficient search algorithm -these algorithms have been based on paradigms from various communities including use of SAT/ SMT solvers (formal methods community), version space algebras (machine learning community), and A*-style goal-directed heuristics (AI community). This paper describes some effective user interaction models and algorithmic methodologies for synthesis from examples while discussing synthesizers for a variety of artifacts ranging from tricky bitvector algorithms, spreadsheet macros for automating repetitive data manipulation tasks, ruler/ compass based geometry constructions, algebraic identities, and predictive intellisense for repetitive drawings and mathematical terms.

关键词： Program Synthesis Inductive Synthesis End User programming Intelligent Tutoring Systems Domain Specific Languages programming by example

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