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S3: Syntax- and Semantic-Guided Repair Synthesis via programming by examples 2017

S3: Syntax- and Semantic-Guided Repair Synthesis via Program...

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

作者： Le, Xuan-Bach D. Chu, Duc-Hiep Lo, David Le Goues, Claire Visser, Willem Singapore Management Univ Singapore Singapore IST Austria Klosterneuburg Austria Carnegie Mellon Univ Pittsburgh PA 15213 USA Stellenbosch Univ Stellenbosch South Africa

ISBN: (纸本)9781450351058

A notable class of techniques for automatic program repair is known as semantics-based. Such techniques, e.g., Angelix, infer semantic specifications via symbolic execution, and then use program synthesis to construct new code that satisfies those inferred specifications. However, the obtained specifications are naturally incomplete, leaving the synthesis engine with a difficult task of synthesizing a general solution from a sparse space of many possible solutions that are consistent with the provided specifications but that do not necessarily generalize. We present S3, a new repair synthesis engine that leverages programming-by-examples methodology to synthesize high-quality bug repairs. The novelty in S3 that allows it to tackle the sparse search space to create more general repairs is three-fold: (1) A systematic way to customize and constrain the syntactic search space via a domain-specific language, (2) An efficient enumeration-based search strategy over the constrained search space, and (3) A number of ranking features based on measures of the syntactic and semantic distances between candidate solutions and the original buggy program. We compare S3's repair effectiveness with state-of-the- art synthesis engines Angelix, Enumerative, and CVC4. S3 can successfully and correctly fix at least three times more bugs than the best baseline on datasets of 52 bugs in small programs, and 100 bugs in real-world large programs.

关键词： Program Repair programming by examples Inductive Synthesis Symbolic Execution

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Inference of Regular Expressions for Text Extraction from examples

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2016年第5期28卷 1217-1230页

作者： Bartoli, Alberto De Lorenzo, Andrea Medvet, Eric Tarlao, Fabiano Univ Trieste Dept Engn & Architecture DIA I-34127 Trieste Italy

A large class of entity extraction tasks from text that is either semistructured or fully unstructured may be addressed by regular expressions, because in many practical cases the relevant entities follow an underlying syntactical pattern and this pattern may be described by a regular expression. In this work, we consider the long-standing problem of synthesizing such expressions automatically, based solely on examples of the desired behavior. We present the design and implementation of a system capable of addressing extraction tasks of realistic complexity. Our system is based on an evolutionary procedure carefully tailored to the specific needs of regular expression generation by examples. The procedure executes a search driven by a multiobjective optimization strategy aimed at simultaneously improving multiple performance indexes of candidate solutions while at the same time ensuring an adequate exploration of the huge solution space. We assess our proposal experimentally in great depth, on a number of challenging datasets. The accuracy of the obtained solutions seems to be adequate for practical usage and improves over earlier proposals significantly. Most importantly, our results are highly competitive even with respect to human operators.

关键词： Genetic programming information extraction programming by examples multiobjective optimization heuristic search

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Neural-Guided Inductive Synthesis of Functional Programs on List Manipulation by Offline Supervised Learning

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IEEE ACCESS 2021年 9卷 71521-71534页

作者： Wang, Yuhong Li, Xin East China Normal Univ Shanghai Key Lab Trustworthy Comp Shanghai 200062 Peoples R China

Synthesizing intended programs from user-specified input-output examples, also known as programming by examples (PBE), is a challenging problem in program synthesis, and has been applied to a wide range of domains. A key challenge in PBE is to efficiently discover a user-intended program in the search space that can be exponentially large. In this work, we propose a method for automatic synthesis of functional programs on list manipulation, by using offline-trained Recurrent Neural Network (RNN) models to guide the program search. We adopt miniKanren, an embedded domain-specific language for flexible relational programming, as an underlying top-down deductive search engine of candidate programs that are consistent with input-output examples. Our approach targets an easy and effective integration of deep learning techniques in making better PBE systems and combines two technical ideas on generating diverse training dataset and designing rich feature embeddings of probable subproblems for synthesis generated by deductive search. The offline-learned model is then used in PBE to guide the top-down deductive search with specific strategies. To practically manipulate data structures of lists, our method synthesizes functional programs with popular higher-order combinators including map, foldl and foldr. We have implemented our method and evaluated it with challenging program synthesis tasks on list manipulation. The experiments show promising results on the performance of our method compared to related state-of-the-art inductive synthesizers.

关键词： Training Task analysis programming Supervised learning Reactive power Automobiles Search engines programming by examples functional programs miniKanren relational programming deep learning deductive search

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Automating Repetitive Code Changes using examples 24

Automating Repetitive Code Changes using Examples

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24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE)

作者： Rolim, Reudismam Univ Fed Campina Grande Campina Grande Brazil

ISBN: (纸本)9781450342186

While adding features, fixing bugs, or refactoring the code, developers may perform repetitive code edits. Although Integrated Development Environments (IDEs) automate some transformations such as renaming, many repetitive edits are performed manually, which is error-prone and time-consuming. To help developers to apply these edits, we propose a technique to perform repetitive edits using examples. The technique receives as input the source code before and after the developer edits some target locations of the change and produces as output the top-ranked program transformation that can be applied to edit the remaining target locations in the codebase. The technique uses a state-of-the-art program synthesis methodology and has three main components: a) a DSL for describing program transformations;b) synthesis algorithms to learn program transformations in this DSL;c) ranking algorithms to select the program transformation with the higher probability of performing the desired repetitive edit. In our preliminary evaluation, in a dataset of 59 repetitive edit cases taken from real C# source code repositories, the technique performed, in 83% of the cases, the intended transformation using only 2.8 examples.

关键词： Program synthesis programming by examples Software evolution

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FlashMeta: A Framework for Inductive Program Synthesis 2015

FlashMeta: A Framework for Inductive Program Synthesis

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ACM SIGPLAN International Conference on Object-Oriented programming, Systems, Languages, and Applications (OOPSLA)

作者： Polozov, Oleksandr Gulwani, Sumit Univ Washington Seattle WA 98195 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781450336895

Inductive synthesis, or programming-by-examples (PBE) is gaining prominence with disruptive applications for automating repetitive tasks in end-user programming. However, designing, developing, and maintaining an effective industrial-quality inductive synthesizer is an intellectual and engineering challenge, requiring 1-2 man-years of effort. Our novel observation is that many PBE algorithms are a natural fall-out of one generic meta-algorithm and the domain-specific properties of the operators in the underlying domain-specific language (DSL). The meta-algorithm propagates example-based constraints on an expression to its subexpressions by leveraging associated witness functions, which essentially capture the inverse semantics of the underlying operator. This observation enables a novel program synthesis methodology called data-driven domain-specific deduction (D-4), where domain-specific insight, provided by the DSL designer, is separated from the synthesis algorithm. Our FlashMeta framework implements this methodology, allowing synthesizer developers to generate an efficient synthesizer from the mere DSL definition (if properties of the DSL operators have been modeled). In our case studies, we found that 10+ existing industrial-quality mass-market applications based on PBE can be cast as instances of D-4. Our evaluation includes reimplementation of some prior works, which in FlashMeta become more efficient, maintainable, and extensible. As a result, FlashMeta-based PBE tools are deployed in several industrial products, including Microsoft PowerShell 3.0 for Windows 10, Azure Operational Management Suite, and Microsoft Cortana digital assistant.

关键词： Algorithms Languages Inductive program synthesis programming by examples frameworks domain-specific languages deductive inference search-based synthesis

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Improving IDE Code Inspections with Tree Automata 2022

Improving IDE Code Inspections with Tree Automata

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30th ACM Joint European Software Engineering Conference / Symposium on the Foundations of Software Engineering (ESEC/FSE)

作者： Lee, Yunjeong Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781450394130

Integrated development environments (IDEs) are equipped with code inspections to warn developers of malformed or incorrect code by analyzing the code's data flow. However, the data flow analysis performed by the IDE code inspections may issue warnings in code where warnings are irrelevant. Existing methods to prevent any bogus warnings are either too conservative - suppressing the same type of warnings in the entire codebase - or are not sustainable as they require adding a set of heuristics to the data flow analysis. We propose a programming-by-example (PBE) framework that synthesizes tree automata (TAs) to suppress bogus warnings in all the code containing the same patterns as the user-provided examples. Experiments with a prototype of the framework demonstrate that several real-world patterns heuristically suppressed in production IDE can be mechanically translated to a TA in a systematic way. Furthermore, we briefly discuss how TA-based solution can be useful in other IDE features such as code refactoring.

关键词： code inspections tree automata programming by examples

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EqFix: Fixing LATEX Equation Errors by examples 8th

EqFix: Fixing LATEX Equation Errors by Examples

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8th International Symposium on Dependable Software Engineering: Theories, Tools and Applications

作者： Zhu, Fengmin He, Fei Tsinghua Univ Sch Software Beijing Peoples R China MoE Key Lab Informat Syst Secur Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China Max Planck Inst Software Syst Saarbrucken Germany

ISBN: (纸本)9783031212123;9783031212130

LATEX is a widely-used document preparation system. Its powerful ability in mathematical equation editing is perhaps the main reason for its popularity in academia. Sometimes, however, even an expert user may spend much time fixing an erroneous equation. In this paper, we present EqFix, a synthesis-based repairing system for LATEX equations. It employs a set of fixing rules and can suggest possible repairs for common errors in LATEX equations. A domain-specific language is proposed for formally expressing the fixing rules. The fixing rules can be automatically synthesized from a set of input-output examples. An extension of relaxers is also introduced to enhance the practicality of EqFix. We evaluate EqFix on real-world examples and find that it can synthesize rules with high generalization ability. Compared with a state-of-the-art string transformation synthesizer, EqFix solved 37% more cases and spent less than half of their synthesis time.

关键词： Domain-specific languages Program synthesis Program repair programming by examples

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FlashExtract: A Framework for Data Extraction by examples 14

FlashExtract: A Framework for Data Extraction by Examples

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35th ACM SIGPLAN Conference on programming Language Design and Implementation (PLDI)

作者： Le, Vu Gulwani, Sumit Univ Calif Davis Davis CA 95616 USA Microsoft Res Redmond Redmond WA USA

ISBN: (纸本)9781450327848

Various document types that combine model and view (e. g., text files, webpages, spreadsheets) make it easy to organize (possibly hierarchical) data, but make it difficult to extract raw data for any further manipulation or querying. We present a general framework FlashExtract to extract relevant data from semi-structured documents using examples. It includes: (a) an interaction model that allows end-users to give examples to extract various fields and to relate them in a hierarchical organization using structure and sequence constructs. (b) an inductive synthesis algorithm to synthesize the intended program from few examples in any underlying domain-specific language for data extraction that has been built using our specified algebra of few core operators (map, filter, merge, and pair). We describe instantiation of our framework to three different domains: text files, webpages, and spreadsheets. On our benchmark comprising 75 documents, FlashExtract is able to extract intended data using an average of 2.36 examples in 0.84 seconds per field.

关键词： Languages Algorithms Human Factors Program Synthesis End-user programming programming by examples

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Automating Repetitive Tasks for the Masses 15

Automating Repetitive Tasks for the Masses

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42nd Annual ACM SIGPLAN-SIGACT Symposium on Principles of programming Languages (POPL 15)

作者： Gulwani, Sumit Microsoft Res Philadelphia PA 19122 USA

ISBN: (纸本)9781450333009

The programming languages (PL) research community has traditionally catered to the needs of professional programmers in the continuously evolving technical industry. However, there is a new opportunity that knocks our doors. The recent IT revolution has resulted in the masses having access to personal computing devices. More than 99% of these computer users are non-programmers and are today limited to being passive consumers of the software that is made available to them. Can we empower these users to more effectively leverage computers for their daily tasks? The formalisms, techniques, and tools developed in the PL and the formal methods research communities can play a pivotal role!

关键词： End-user programming Computer-aided Education programming by examples programming by Natural Language Program Synthesis Domain-specific Languages Search Algorithms

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Flexible Example-Based Program Synthesis on Tree-Structured Function Compositions

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SN Computer Science 2022年第3期3卷 1-15页

作者： Mucsányi, Bálint Gyarmathy, Bálint Czapp, Ádám Pintér, Balázs Faculty of Informatics ELTE Eötvös Loránd University Pázmány Péter sétány 1/C Budapest 1117 Hungary

We introduce a flexible program synthesis system whose task is to predict function compositions that transform given inputs to their corresponding given outputs. We process input lists in a sequential manner, allowing the system to generalize to a wide range of input lengths. We separate the operator and the operand in the lambda functions of the used higher order functions to achieve significantly wider numeric parameter ranges compared to the previous works. The evaluations show that this approach is competitive with state-of-the-art systems, while it is much more flexible in terms of the input length, the parameters of the lambda functions, and the integer range of the inputs and outputs. We extend the system to handle tree-structured function compositions by introducing two additional functions (zip_with, copy) and the ability to represent unfinished function compositions during the synthesis process. The extended system achieves state-of-the-art results while synthesizing complex function compositions with multiple forks. We believe that flexibility in these aspects is an important step towards solving real-world problems with example-based program synthesis. © 2022, The Author(s).

关键词： Beam search Gated recurrent unit Program synthesis programming by examples Recurrent neural network Tree-structured composition

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