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Data Formulator: AI-Powered Concept-Driven Visualization Authoring

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024年第1期30卷 1128-1138页

作者： Wang, Chenglong Thompson, John Lee, Bongshin Microsoft Res Redmond WA 98052 USA

With most modern visualization tools, authors need to transform their data into tidy formats to create visualizations they want. Because this requires experience with programming or separate data processing tools, data transformation remains a barrier in visualization authoring. To address this challenge, we present a new visualization paradigm, concept binding, that separates high-level visualization intents and low-level data transformation steps, leveraging an AI agent. We realize this paradigm in Data Formulator, an interactive visualization authoring tool. With Data Formulator, authors first define data concepts they plan to visualize using natural languages or examples, and then bind them to visual channels. Data Formulator then dispatches its AI-agent to automatically transform the input data to surface these concepts and generate desired visualizations. When presenting the results (transformed table and output visualizations) from the AI agent, Data Formulator provides feedback to help authors inspect and understand them. A user study with 10 participants shows that participants could learn and use Data Formulator to create visualizations that involve challenging data transformations, and presents interesting future research directions.

关键词： Data visualization Temperature distribution Urban areas Visualization Transforms Histograms Libraries AI visualization authoring data transformation programming by example natural language large language model

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Generalized planning as heuristic search: A new planning search-space that leverages pointers over objects

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ARTIFICIAL INTELLIGENCE 2024年 330卷

作者： Segovia-Aguas, Javier Jimenez, Sergio Jonsson, Anders Univ Pompeu Fabra Fabra Spain Univ Politecn Valencia Valencia Spain

Planning as heuristic search is one of the most successful approaches to classical planning but unfortunately, it does not trivially extend to Generalized Planning (GP);GP aims to compute algorithmic solutions that are valid for a set of classical planning instances from a given domain, even if these instances differ in their number of objects, the initial and goal configuration of these objects and hence, in the number (and possible values) of the state variables. State -space search, as it is implemented by heuristic planners, becomes then impractical for GP. In this paper we adapt the planning as heuristic search paradigm to the generalization requirements of GP, and present the first native heuristic search approach to GP. First, the paper introduces a new pointerbased solution space for GP that is independent of the number of classical planning instances in a GP problem and the size of those instances (i.e. the number of objects, state variables and their domain sizes). Second, the paper defines an upgraded version of our GP algorithm, called Best -First Generalized Planning (BFGP), that implements a best -first search in our pointer -based solution space for GP. Lastly, the paper defines a set of evaluation and heuristic functions for BFGP that assess the structural complexity of the candidate GP solutions, as well as their fitness to a given input set of classical planning instances. The computation of these evaluation and heuristic functions does not require grounding states or actions in advance. Therefore our GP as heuristic search approach can handle large sets of state variables with large numerical domains, e.g. integers.

关键词： Generalized planning Classical planning Heuristic search Planning and learning Domain-specific control knowledge Program synthesis programming by example

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Inductive Program Synthesis via Iterative Forward-Backward Abstract Interpretation

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2023年第PLDI期7卷 1657-1681页

作者： Yoon, Yongho Lee, Woosuk Yi, Kwangkeun Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Hanyang Univ Dept Comp Sci & Engn Seoul South Korea

A key challenge in example-based program synthesis is the gigantic search space of programs. To address this challenge, various work proposed to use abstract interpretation to prune the search space. However, most of existing approaches have focused only on forward abstract interpretation, and thus cannot fully exploit the power of abstract interpretation. In this paper, we propose a novel approach to inductive program synthesis via iterative forward-backward abstract interpretation. The forward abstract interpretation computes possible outputs of a program given inputs, while the backward abstract interpretation computes possible inputs of a program given outputs. By iteratively performing the two abstract interpretations in an alternating fashion, we can effectively determine if any completion of each partial program as a candidate can satisfy the input-output examples. We apply our approach to a standard formulation, syntax-guided synthesis (SyGuS), thereby supporting a wide range of inductive synthesis tasks. We have implemented our approach and evaluated it on a set of benchmarks from the prior work. The experimental results show that our approach significantly outperforms the state-of-the-art approaches thanks to the sophisticated abstract interpretation techniques.

关键词： Program Synthesis programming by example Abstract Interpretation

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Inductive Synthesis of Structurally Recursive Functional Programs from Non-recursive Expressions

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2023年第POPL期7卷 2048-2078页

作者： Lee, Woosuk Cho, Hangyeol Hanyang Univ Dept Comp Sci & Engn Seoul South Korea

We present a novel approach to synthesizing recursive functional programs from input-output examples. Synthesizing a recursive function is challenging because recursive subexpressions should be constructed while the target function has not been fully defined yet. We address this challenge by using a new technique we call block-based pruning. A block refers to a recursion- and conditional-free expression (i.e., straight-line code) that yields an output from a particular input. We first synthesize as many blocks as possible for each input-output example, and then we explore the space of recursive programs, pruning candidates that are inconsistent with the blocks. Our method is based on an efficient version space learning, thereby effectively dealing with a possibly enormous number of blocks. In addition, we present a method that uses sampled input-output behaviors of library functions to enable a goal-directed search for a recursive program using the library. We have implemented our approach in a system called Trio and evaluated it on synthesis tasks from prior work and on new tasks. Our experiments show that Trio outperforms prior work by synthesizing a solution to 98% of the benchmarks in our benchmark suite.

关键词： programming by example Recursive Functional Programs Synthesis

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Mobius: Synthesizing Relational Queries with Recursive and Invented Predicates

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2023年第OOPSLA期7卷 1394–1417页

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

Synthesizing relational queries from data is challenging in the presence of recursion and invented predicates. We propose a fully automated approach to synthesize such queries. Our approach comprises of two steps: it first synthesizes a non-recursive query consistent with the given data, and then identifies recursion schemes in it and thereby generalizes to arbitrary data. This generalization is achieved by an iterative predicate unification procedure which exploits the notion of data provenance to accelerate convergence. In each iteration of the procedure, a constraint solver proposes a candidate query, and a query evaluator checks if the proposed program is consistent with the given data. The data provenance for a failed query allows us to construct additional constraints for the constraint solver and refine the search. We have implemented our approach in a tool named MOBIUS. On a suite of 21 challenging recursive query synthesis tasks, MOBIUS outperforms three state-of-the-art baselines GENSYNTH, ILASP, and POPPER, both in terms of runtime and accuracy. We also demonstrate that the synthesized queries generalize well to unseen data.

关键词： programming by example example-guided synthesis recursive program synthesis

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Repairing Regular Expressions for Extraction

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2023年第PLDI期7卷 1633-1656页

作者： Chida, Nariyoshi Terauchi, Tachio Waseda Univ NTT Social Informat Labs Tokyo Japan Waseda Univ Tokyo Japan

While synthesizing and repairing regular expressions (regexes) based on programming-by-examples (PBE) methods have seen rapid progress in recent years, all existing works only support synthesizing or repairing regexes for membership testing, and the support for extraction is still an open problem. This paper fills the void by proposing the first PBE-based method for synthesizing and repairing regexes for extraction. Our work supports regexes that have real-world extensions such as backreferences and lookarounds. The extensions significantly affect the PBE-based synthesis and repair problem. In fact, we show that there are unsolvable instances of the problem if the synthesized regexes are not allowed to use the extensions, i.e., there is no regex without the extensions that correctly classify the given set of examples, whereas every problem instance is solvable if the extensions are allowed. This is in stark contrast to the case for the membership where every instance is guaranteed to have a solution expressible by a pure regex without the extensions. The main contribution of the paper is an algorithm to solve the PBE-based synthesis and repair problem for extraction. Our algorithm builds on existing methods for synthesizing and repairing regexes for membership testing, i.e., the enumerative search algorithms with SMT constraint solving. However, significant extensions are needed because the SMT constraints in the previous works are based on a non-deterministic semantics of regexes. Non-deterministic semantics is sound for membership but not for extraction, because which substrings are extracted depends on the deterministic behavior of actual regex engines. To address the issue, we propose a new SMT constraint generation method that respects the deterministic behavior of regex engines. For this, we first define a novel formal semantics of an actual regex engine as a deterministic big-step operational semantics, and use it as a basis to design the new SMT constraint g

关键词： Regular Expression programming by example Program Repair

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Repairing Regex-Dependent String Functions 24

Repairing Regex-Dependent String Functions

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39th ACM/IEEE International Conference on Automated Software Engineering (ASE)

作者： Chida, Nariyoshi Terauchi, Tachio NTT Social Informat Labs Tokyo Japan Waseda Univ Tokyo Japan

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Regex-dependent string functions are string functions that take regular expressions (regexes) as parameters and are popular means of manipulating strings. They are frequently used for, e.g., string transformation and substring search. Despite the importance, writing these functions is far from easy. To rectify this situation, recent research made significant progress by proposing automated methods for synthesizing regexes based on programming by examples (PBE). However, there still is a gap between these methods and the goal of synthesizing regex-dependent string functions. First, the existing methods focus on whole-string matching, whereas most regex-dependent string functions adopt substring matching. Second, the existing methods focus only on the regex, but many commonly used regex-dependent string functions, such as replace and replaceAll, also take as parameter a replacement to specify how the substrings matched to the regex will be replaced. This paper fills the gap by presenting the first PBE-based method for repairing regex-dependent string functions. Like the recent methods for regex synthesis, our algorithm builds on enumerative search with pruning and SMT constraint solving, but with extensions to support substring matching and replacement. The main challenge is the large search space. We address the challenge by novel ideas such as incorporation of origin information in examples to identify the locations of substrings to be matched, a new substring-context-aware pruning technique, and a novel use of SMT constraints to insert captures that can be referred from the replacement. Additionally, we identify a novel necessary and sufficient condition that can be used to detect and filter unrepairable instances. We implemented our algorithm as a prototype tool called R2-DS and evaluated it on real-world benchmarks. Results show that our algorithm efficiently repairs the bugs in the real world and finds high-quality repairs.

关键词： Regular Expression programming by example Program Repair

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VRDSynth: Synthesizing Programs for Multilingual Visually Rich Document Information Extraction 2024

VRDSynth: Synthesizing Programs for Multilingual Visually Ri...

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33rd ACM SIGSOFT International Conference on Software Testing and Analysis (ISSTA)

作者： Thanh-Dat Nguyen Tung Do-Viet Hung Nguyen-Duy Tuan-Hai Luu Le, Hung Le, Bach Thongtanunam, Patanamon Univ Melbourne Melbourne Vic Australia Cinnamon AI Ho Chi Minh City Vietnam Deakin Univ Geelong Vic Australia

ISBN: (纸本)9798400706127

Businesses often need to query visually rich documents (VRDs), e.g., purchase receipts, medical records, and insurance forms, among many other forms from multiple vendors, to make informed decisions. As such, several techniques have been proposed to automatically extract independent entities of interest front VRDs such as extracting price tags from purchase receipts, etc. However, for extracting semantically linked entities, such as finding corresponding price tags for each item, these techniques either have limited capability in handling new layouts, e.g., template-based approaches, or require extensive amounts of pre-training data and do not perform well, e.g., deep-learning approaches. In this work, we introduce a program synthesis method, namely VRDSYNTH;to automatically generate programs to extract entity relations from multilingual VRDs. Two key novelties, which empower VRDSYNTH to tackle flexible layouts while requiring no pre-training data for extracting entity relations, include: (1) a new domain-specific language (DSL) to effectively capture the spatial and textual relations between document entities, and (2) a novel synthesis algorithm that makes use of frequent spatial relations between entities to construct initial programs, equivalent reduction to prune the search space, and a combination of positive, negative, and mutually exclusive programs to improve the coverage of programs. We experiment on two popular VRD understanding benchmarks, namely FUNSD and XFUND, on the semantic entity linking task, consisting of 1,592 forms in 8 different languages. VRDSYNTH, despite having no prior pre-training data, outperforms the s tate-of-theart pre-trained deep-learning approaches- LayoutXLM, I nfoXLMB, and XLMRobertaBase in 5, 6 and 7 out of 8 languages, respectively. Noticeably, in English. VRDSYNTH improves 42% of Fl score over LayoutXLM while being complementary to LayoutXLM in 7/8 languages. We further extend VRDSYNTH with automated table recognition, namely V

关键词： Automatic programming programming by example Program Synthesis Information Extraction Visually-Rich Document

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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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RoboART: Artistic Robot programming in Mixed Reality

RoboART: Artistic Robot Programming in Mixed Reality

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IEEE Conference on Virtual Reality and 3D User Interfaces (VR)

作者： Fronchetti, Felipe Popiela, Miles Spinola, Rodrigo Brixey, Shawn Virginia Commonwealth Univ Sch Arts Richmond VA 23284 USA Virginia Commonwealth Univ Coll Engn Richmond VA 23284 USA

ISBN: (纸本)9798350374490;9798350374506

Articulated robots are attracting the attention of artists worldwide. Due to their precise, tireless, and efficient nature, robots are now being deployed in different forms of creative expression, such as sculpting, choreography, immersive environments, and cinematography. While there is a growing interest among artists in robotics, programming such machines is a challenge for most professionals in the field, as robots require extensive coding experience and are primarily designed for industrial applications and environments. To enable artists to incorporate robots in their projects, we propose an end-user-friendly robot programming solution using an intuitive spatial computing environment designed for Microsoft Hololens 2. In our application, the robot movements are synchronized with a hologram via network communication. Using natural hand gestures, users can manipulate, animate, and record the hologram similar to 3D animation software, including the advantages of mixed reality interaction. Our solution not only gives artists the ability to translate their creative ideas and movements to an industrial machine but also makes human-robot interaction safer, as robots can now be accurately and effectively operated from a distance. We consider this an important step in a more human-driven robotics community, allowing creators without robot programming experience to easily script and perform complex sequences of robotic movement in service of new arts applications. Making robots more collaborative and safer for humans to interact with dramatically increases their utility, exposure, and potential for social interaction, opens new markets, expands creative industries, and directly locates them in highly visible public spaces.

关键词： Software and its engineering Software notations and tools Context specific languages programming by example Applied computing Arts and humanities Performing arts

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