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COOL: A Constraint Object-Oriented Logic programming Language and its Neural-Symbolic Compilation System

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arXiv 2023年

作者： Han, Jipeng Beijing Huagui Technology China

This paper explores the integration of neural networks with logic programming, addressing the longstanding challenges of combining the generalization and learning capabilities of neural networks with the precision of symbolic logic. Traditional attempts at this integration have been hampered by difficulties in initial data acquisition, the reliability of undertrained networks, and the complexity of reusing and augmenting trained models. To overcome these issues, we introduce the COOL (Constraint Object-Oriented Logic) programming language, an innovative approach that seamlessly combines logical reasoning with neural network technologies. COOL is engineered to autonomously handle data collection, mitigating the need for user-supplied initial data. It incorporates user prompts into the coding process to reduce the risks of undertraining and enhances the interaction among models throughout their lifecycle to promote the reuse and augmentation of networks. Furthermore, the foundational principles and algorithms in COOL’s design and its compilation system could provide valuable insights for future developments in programming languages and neural network architectures. © 2023, CC BY.

关键词： computer programming languages

ECMAScript - The journey of a programming language from an idea to a standard

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arXiv 2023年

作者： Vepsäläinen, Juho Aalto University Finland

A significant portion of the web is powered by ECMAScript. As a web technology, it is ubiquitous and available on most platforms natively or through a web browser. ECMAScript is the dominant language of the web, but at the same time, it was not designed as such. The story of ECMAScript is a story of the impact of standardization on the popularity of technology. Simultaneously, the story shows how external pressures can shape a programming language and how politics can mar the evolution of a standard. In this article, we will go through the movements that led to the dominant position of ECMAScript, evaluate the factors leading to it, and consider its evolution using the Futures Triangle framework and the theory of standards wars. Copyright © 2023, The Authors. All rights reserved.

关键词： computer programming languages

Contrastive Prompt Learning-based Code Search based on Interaction Matrix

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arXiv 2023年

作者： Zhang, Yubo Liu, Yanfang Fan, Xinxin Lu, Yunfeng School of Computer Science and Engineering Beihang University Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Reliability and Systems Engineering Beihang University Beijing China

Code search aims to retrieve the code snippet that highly matches the given query described in natural language. Recently, many code pre-training approaches have demonstrated impressive performance on code search. However, existing code search methods still suffer from two performance constraints: inadequate semantic representation and the semantic gap between natural language (NL) and programming language (PL). In this paper, we propose CPLCS, a contrastive prompt learning-based code search method based on the cross-modal interaction mechanism. CPLCS comprises:(1) PL-NL contrastive learning, which learns the semantic matching relationship between PL and NL representations;(2) a prompt learning design for a dual-encoder structure that can alleviate the problem of inadequate semantic representation;(3) a cross-modal interaction mechanism to enhance the fine-grained mapping between NL and PL. We conduct extensive experiments to evaluate the effectiveness of our approach on a real-world dataset across six programming languages. The experiment results demonstrate the efficacy of our approach in improving semantic representation quality and mapping ability between PL and NL. Copyright © 2023, The Authors. All rights reserved.

关键词： computer programming languages

Programmers' affinity to languages 2

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Programmers' affinity to languages

2nd International computer programming Education Conference, ICPEC 2021

作者： Neto, Alvaro Costa Araújo, Cristiana Pereira, Maria João Varanda Henriques, Pedro Rangel Federal Institute of Education Science and Technology of São Paulo Barretos Brazil Centro ALGORITMI Departamento de Informática University of Minho Campus Gualtar Braga Portugal Research Centre in Digitalization and Intelligent Robotics Polytechnic Institute of Bragança Portugal

ISBN: (纸本)9783959771948

Students face several challenges when learning computer programming languages, a central topic to acquire programming skills. While those challenges that present a predominantly technical nature have been intensely studied by researchers along the years, the ones that are concerned with qualitative, and personal aspects have not. Affinity to a programming language is one of the many personal factors that may contribute to surpass these qualitative aspects that describe the difficulties that students face. From this point-of-view, this paper presents a proposal for treating and studying programmers' affinity to programming languages as an important factor for learning computer programming. It also reports a preliminary questionnaire conducted on a master's degree class at Universidade do Minho that showed that affinity may have a broader relation to learning computer programming than anticipated. Finally, a set of relevant questions are stated to compose a future inquiry aimed at deepening the knowledge on the affinity between programmers and languages, paving the way for following research. © Alvaro Costa Neto, Cristiana Araújo, Maria João Varanda Pereira, and Pedro Rangel Henriques;licensed under Creative Commons License CC-BY 4.0 Second International computer programming Education Conference (ICPEC 2021).

关键词： computer programming languages

Kind Inference for the FreeST programming Language

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arXiv 2023年

作者： Almeida, Bernardo Mordido, Andreia Vasconcelos, Vasco T. LASIGE Faculdade de Ciências Universidade de Lisboa Portugal

We present a kind inference algorithm for the FREEST programming language. The input to the algorithm is FREEST source code with (possibly part of) kind annotations replaced by kind variables. The algorithm infers concrete kinds for all kind variables. We ran the algorithm on the FREEST test suite by first replacing kind annotation on all type variables by fresh kind variables, and concluded that the algorithm correctly infers all kinds. Non surprisingly, we found out that programmers do not choose the most general kind in 20% of the cases. © 2023, CC BY.

关键词： computer programming languages

Neural Network Exemplar Parallelization with Go

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arXiv 2023年

作者： Wiesinger, Georg Schikuta, Erich Faculty of Computer Science University of Vienna Vienna Austria

This paper presents a case for exemplar parallelism of neural networks using Go as parallelization framework. Further it is shown that also limited multi-core hardware systems are feasible for these parallelization tasks, as notebooks and single board computer systems. The main question was how much speedup can be generated when using concurrent Go goroutines specifically. A simple concurrent feedforward network for MNIST [1] digit recognition with the programming language Go [2]-[5] was created to find the answer. The first findings when using a notebook (Lenovo Yoga 2) showed a speedup of 252% when utilizing 4 goroutines. Testing a single board computer (Banana Pi M3) delivered more convincing results: 320% with 4 goroutines, and 432% with 8 *** Codes 68T07 © 2023, CC BY.

关键词： computer programming languages

Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit

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arXiv 2023年

作者： Wan, Yao He, Yang Bi, Zhangqian Zhang, Jianguo Zhang, Hongyu Sui, Yulei Xu, Guandong Jin, Hai Yu, Philip S. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Simon Fraser University Vancouver Canada School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Salesforce Research United States Chongqing University China University of New South Wales Australia University of Technology Sydney Australia University of Illinois at Chicago Chicago United States

Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and productivity of computer programming. Currently, there is already a thriving research community focusing on code intelligence, with efforts ranging from software engineering, machine learning, data mining, natural language processing, and programming languages. In this paper, we conduct a comprehensive literature review on deep learning for code intelligence, from the aspects of code representation learning, deep learning techniques, and application tasks. We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models. In particular, we inspect the existing code intelligence models under the basis of code representation learning, and provide a comprehensive overview to enhance comprehension of the present state of code intelligence. Furthermore, we publicly release the source code and data resources to provide the community with a ready-to-use benchmark, which can facilitate the evaluation and comparison of existing and future code intelligence models (https://***). At last, we also point out several challenging and promising directions for future research. Copyright © 2023, The Authors. All rights reserved.

关键词： computer programming languages

Grouping Source Code by Solution Approaches — Improving Feedback in programming Courses 14

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Grouping Source Code by Solution Approaches — Improving Fee...

14th International Conference on Educational Data Mining, EDM 2023

作者： Höppner, Frank Ostfalia University of Applied Sciences Germany

ISBN: (纸本)9781733673624

Various similarity measures for source code have been proposed, many rely on edit- or tree-distance. To support a lecturer in quickly assessing live or online exercises with respect to approaches taken by the students, we compare source code on a more abstract, semantic level. Even if novice student’s solutions follow the same idea, their code length may vary considerably – which greatly misleads edit and tree distance approaches. We propose an alternative similarity measure based on variable usage paths (VUP), that is, we use the way how variables are used in the code to elaborate code similarity. The final stage of the measure involves a matching of variables in functions based on how the variable is used by the instructions. A preliminary evaluation on real data is presented. © EDM *** rights reserved.

关键词： computer programming languages

Operational semantics with hierarchical abstract syntax graphs 11

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Operational semantics with hierarchical abstract syntax grap...

11th International Workshop on Computing with Terms and Graphs, TERMGRAPH 2020

作者： Ghica, Dan R. Huawei Research Edinburgh University of Birmingham United Kingdom

This is a motivating tutorial introduction to a semantic analysis of programming languages using a graphical language as the representation of terms, and graph rewriting as a representation of reduction rules. We show how the graphical language automatically incorporates desirable features, such as α-equivalence and how it can describe pure computation, imperative store, and control features in a uniform framework. The graph semantics combines some of the best features of structural operational semantics and abstract machines, while offering powerful new methods for reasoning about contextual equivalence. All technical details are available in an extended technical report by Muroya and the author [11] and in Muroya’s doctoral dissertation [21]. © Dan R. Ghica

关键词： computer programming languages