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36th ACM SIGPLAN Conference on programming Language Design and Implementation

作者： Leung, Alan Sarracino, John Lerner, Sorin Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781450334686

Despite decades of research on parsing, the construction of parsers remains a painstaking, manual process prone to subtle bugs and pitfalls. We present a programming-by-example framework called Parsify that is able to synthesize a parser from input/output examples. The user does not write a single line of code. To achieve this, Parsify provides: (a) an iterative algorithm for synthesizing and refining a grammar one example at a time, (b) an interface that provides immediate visual feedback in response to changes in the grammar being refined, and (c) a graphical mechanism for specifying example parse trees using only textual selections. We empirically demonstrate the viability of our approach by using Parsify to construct parsers for source code drawn from Verilog, SQL, Apache, and Tiger.

关键词： Algorithms Human Factors Languages Parsing programming by example Program Synthesis

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Multi-modal Synthesis of Regular Expressions 2020

Multi-modal Synthesis of Regular Expressions

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41st ACM SIGPLAN Conference on programming Language Design and Implementation (PLDI)

作者： Chen, Qiaochu Wang, Xinyu Ye, Xi Durrett, Greg Dillig, Isil Univ Texas Austin Austin TX 78712 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781450376136

In this paper, we propose a multi-modal synthesis technique for automatically constructing regular expressions (regexes) from a combination of examples and natural language. Using multiple modalities is useful in this context because natural language alone is often highly ambiguous, whereas examples in isolation are often not sufficient for conveying user intent. Our proposed technique first parses the English description into a so-called hierarchical sketch that guides our programming-by-example (PBE) engine. Since the hierarchical sketch captures crucial hints, the PBE engine can leverage this information to both prioritize the search as well as make useful deductions for pruning the search space. We have implemented the proposed technique in a tool called Regel and evaluate it on over three hundred regexes. Our evaluation shows that Regel achieves 80% accuracy whereas the NLP-only and PBE-only baselines achieve 43% and 26% respectively. We also compare our proposed PBE engine against an adaptation of AlphaRegex, a state-of-the-art regex synthesis tool, and show that our proposed PBE engine is an order of magnitude faster, even if we adapt the search algorithm of AlphaRegex to leverage the sketch. Finally, we conduct a user study involving 20 participants and show that users are twice as likely to successfully come up with the desired regex using Regel compared to without it.

关键词： Program Synthesis programming by Natural Languages programming by example Regular Expression

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Synthesis of rewrite programs by higher-order and semantic unification

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New Generation Computing 1991年第4期8卷 403-420页

作者： Hagiya, Masami Research Institute for Mathematical Sciences Kyoto University Kyoto 606 Sakyo Japan

This paper presents a framework for synthesizing rewrite programs using higher-order and semantic unification. Many problems in computer science and artificial intelligence can be formalized as problems of higher-order unification. Among such problems is first-order anti-unification. In this paper, we show that first-order anti-unification can be regarded as a second-order matching problem and solved by the algorithm for higher-order unification. The problem of synthesizing a program from example input-output pairs can be considered as an extension of anti-unification because the problem of instantiating a program schema by examples consists of a pair of anti-unification problems. We show that higher-order unification can also be used to instantiate a recursive program schema and synthesize a rewrite program from examples. For expressing program schemata for rewrite programs, we extend lambda terms by introducing a construct called pseudo-abstraction. In order to synthesize a rewrite program that refers to existing ones as libraries, we merge higher-order unification and semantic unification because existing rewrite programs comprise a term rewriting system. © 1991 Ohmsha, Ltd. and Springer.

关键词： Anti-Unification High-Order Unification Inductive Inference Narrowing programming by example Semantic Unification Term Rewriting System

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RULER: Prebugging with Proxy-Based programming

RULER: Prebugging with Proxy-Based Programming

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IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

作者： Repenning, Alexander Basawapatna, Ashok FHNW Switzerland Sch Educ Brugg Switzerland SUNY Old Westbury Math Comp & Informat Sci Long Isl City NY USA

ISBN: (纸本)9798350376920;9798350366136

While block-based programming has successfully eliminated critical syntactic barriers to programming, it remains unclear how effectively it aids in overcoming semantic, logical, and pragmatic programming challenges that hinder computational thinking. These challenges are likely to far outweigh the syntactic ones. With the goal of creating a highly accessible programming tool for young students using mobile devices, we explored the concept of pragmatic prebugging to begin addressing these challenges. By pragmatic prebugging, we refer to proactive debugging tools designed to prevent logical errors. This article introduces *** as a Computational Thinking Tool with built-in pragmatic prebugging, enabling novice programmers to create games through a paradigm we call proxy-based programming. A small study exploring error rates found statistically significant performance improvements of proxy-based programming compared to block-based programming.

关键词： block-based programming computational thinking programming by example mobile computing

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Unsupervised Neural Controller for Reinforcement Learning Action-Selection: Learning To Represent Knowledge 12

Unsupervised Neural Controller for Reinforcement Learning Ac...

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Symposium on Neural Network Applications in Electrical Engineering (NEUREL)

作者： Gkiokas, Alexandros Cristea, Alexandra I. Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England

ISBN: (纸本)9781479958887

Constructing the correct Conceptual Graph representing some textual information requires a series of decisions, defined by vertex or edge creation. The process of creating Conceptual Graphs involves semiotics: the semantics, pragmatics and syntactics of the information, as well as graph structuralism and isomorphic projection, all described as decisions of a learning agent or system. The actual process taught from demonstrations of a human user, is known as Semantic Parsing, and is learnt by the agent through the novel fusion of Reinforcement Learning (RL) and Restricted Boltzmann Machines (RBM). Herein we showcase the design of such an agent in a theoretical manner, in order to define the background mechanisms which will learn how to parse information and correctly project it onto Conceptual Graphs.

关键词： Complex systems conceptual graphs deep learning programming by example reinforcement learning restricted Boltzmann machines

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Synthesizing Web Element Locators 15

Synthesizing Web Element Locators

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30th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)

作者： Bajaj, Kartik Pattabiraman, Karthik Mesbah, Ali Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9781509000258

To programmatically interact with the user interface of a web application, element locators are used to select and retrieve elements from the Document Object Model (DOM). Element locators are used in JavaScript code, Cascading stylesheets, and test cases to interact with the runtime DOM of the webpage. Constructing these element locators is, however, challenging due to the dynamic nature of the DOM. We find that locators written by web developers can be quite complex, and involve selecting multiple DOM elements. We present an automated technique for synthesizing DOM element locators using examples provided interactively by the developer. The main insight in our approach is that the problem of synthesizing complex multi-element locators can be expressed as a constraint solving problem over the domain of valid DOM states in a web application. We implemented our synthesis technique in a tool called LED, which provides an interactive drag and drop support inside the browser for selecting positive and negative examples. We find that LED supports at least 86% of the locators used in the JavaScript code of deployed web applications, and that the locators synthesized by LED have a recall of 98% and a precision of 63%. LED is fast, taking only 0.23 seconds on average to synthesize a locator.

关键词： Program synthesis programming by example Element locators CSS selectors Web applications

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Evolutionary Learning of Syntax Patterns for Genic Interaction Extraction 15

Evolutionary Learning of Syntax Patterns for Genic Interacti...

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17th Genetic and Evolutionary Computation Conference (GECCO)

作者： Bartoli, Alberto De Lorenzo, Andrea Medvet, Eric Tarlao, Fabiano Virgolin, Marco Univ Trieste DIA I-34127 Trieste Italy

ISBN: (纸本)9781450334723

There is an increasing interest in the development of techniques for automatic relation extraction from unstructured text. The biomedical domain, in particular, is a sector that may greatly benefit from those techniques due to the huge and ever increasing amount of scientific publications describing observed phenomena of potential clinical interest. In this paper, we consider the problem of automatically identifying sentences that contain interactions between genes and proteins, based solely on a dictionary of genes and proteins and a small set of sample sentences in natural language. We propose an evolutionary technique for learning a classifier that is capable of detecting the desired sentences within scientific publications with high accuracy. The key feature of our proposal, that is internally based on Genetic programming, is the construction of a model of the relevant syntax patterns in terms of standard part-of-speech annotations. The model consists of a set of regular expressions that are learned automatically despite the large alphabet size involved. We assess our approach on two realistic datasets and obtain 74% accuracy, a value sufficiently high to be of practical interest and that is in line with significant baseline methods.

关键词： Regular Expressions Genetic programming programming by example Machine Learning

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LED: Tool for Synthesizing Web Element Locators 15

LED: Tool for Synthesizing Web Element Locators

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30th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)

作者： Bajaj, Kartik Pattabiraman, Karthik Mesbah, Ali Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9781509000258

Web applications are growing fast in popularity and complexity. One of the major problems faced by web developers is writing JavaScript code that can retrieve Document Object Model (DOM) tree elements, and is consistent among multiple DOM states. We attempt to solve this problem by automatically synthesizing JavaScript code that interacts with the DOM. We present an automated tool called LED, to analyze the DOM elements, and synthesize code to select the DOM elements based on the DOM hierarchy as well as the nature of task that the user wants to perform. LED provides an interactive drag and drop support inside the browser for selecting positive and negative examples of DOM elements. We find that LED supports at least 86% of the locators used in the JavaScript code of deployed web applications, and that the locators synthesized by LED have a recall of 98% and a precision of 63%. LED is fast, taking only 0.23 seconds on average to synthesize a locator.

关键词： Program synthesis programming by example Element locators CSS selectors Web applications

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Using Ensembles of Decision Trees to Automate Repetitive Tasks in Web Applications

Using Ensembles of Decision Trees to Automate Repetitive Tas...

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2nd ACM SIGCHI Symposium on Engineering Interactive Computing Systems

作者： Bray, Zachary Kristensson, Per Ola Univ Cambridge Comp Lab Cambridge CB3 0FD England

ISBN: (纸本)9781450300834

Web applications such as web-based email, spreadsheets and form filling applications have become ubiquitous. However, many of the tasks that users try to accomplish with such web applications are highly repetitive. In this paper we present the design of a system we have developed that learns and thereafter automates users' repetitive tasks in web applications. Our system infers users' intentions using an ensemble of decision trees. This enables it to handle branching, generalization and recurrent changes of relative and absolute positions. Our evaluation shows that our system converges to the correct solution after 3-8 iterations when the pattern is noise-free, and after 3-14 iterations for a noise level between 5-35%.

关键词： End-user programming programming by example

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PYEVOLVE: Automating Frequent Code Changes in Python ML Systems 23

PYEVOLVE: Automating Frequent Code Changes in Python ML Syst...

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45th IEEE/ACM International Conference on Software Engineering (ICSE)

作者： Dilhara, Malinda Dig, Danny Ketkar, Ameya Univ Colorado Boulder Boulder CO 80309 USA Univ Colorado Boulder JetBrains Res Boulder CO USA Uber Technol Inc San Francisco CA USA

ISBN: (纸本)9781665457019

Because of the naturalness of software and the rapid evolution of Machine Learning (ML) techniques, frequently repeated code change patterns (CPATs) occur often. They range from simple API migrations to changes involving several complex control structures such as for loops. While manually performing CPATs is tedious, the current state-of-the-art techniques for inferring transformation rules are not advanced enough to handle unseen variants of complex CPATs, resulting in a low recall rate. In this paper we present a novel, automated workflow that mines CPATs, infers the transformation rules, and then transplants them automatically to new target sites. We designed, implemented, evaluated and released this in a tool, PYEVOLVE. At its core is a novel data-flow, control-flow aware transformation rule inference engine. Our technique allows us to advance the state-of-the-art for transformation-by-example tools;without it, 70% of the code changes that PYEVOLVE transforms would not be possible to automate. Our thorough empirical evaluation of over 40,000 transformations shows 97% precision and 94% recall. By accepting 90% of CPATs generated by PYEVOLVE in famous open-source projects, developers confirmed its changes are useful.

关键词： Machine Learning Program synthesis Program transformation programming by example Python Repetitive code changes Transformation by example

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