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Generative Datalog with Stable Negation

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arXiv 2022年

作者： Alviano, Mario Lanzinger, Matthias Morak, Michael Pieris, Andreas University of Oxford United Kingdom University of Calabria Italy University of Klagenfurt Austria University of Edinburgh University of Cyprus Cyprus

Extending programming languages with stochastic behaviour such as probabilistic choices or random sampling has a long tradition in computer science. A recent development in this direction is a declarative probabilistic programming language, proposed by Bárány et al. in 2017, which operates on standard relational databases. In particular, Bárány et al. proposed generative Datalog, a probabilistic extension of Datalog that allows sampling from discrete probability distributions. Intuitively, the output of a generative Datalog program Π on an input database D is a probability space over the minimal models of D and Π, the so-called possible outcomes. This is a natural generalization of the (deterministic) semantics of Datalog, where the output of a program on a database is their unique minimal model. A natural question to ask is how generative Datalog can be enriched with the useful feature of negation, which in turn leads to a strictly more expressive declarative probabilistic programming language. In particular, the challenging question is how the probabilistic semantics of generative Datalog with negation can be robustly defined. Our goal is to provide an answer to this question by interpreting negation according to the stable model semantics. © 2022, CC BY.

关键词： computer programming languages

M2TS: Multi-Scale Multi-Modal Approach Based on Transformer for Source Code Summarization

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arXiv 2022年

作者： Gao, Yuexiu Lyu, Chen Shandong Normal University Jinan China

Source code summarization aims to generate natural language descriptions of code snippets. Many existing studies learn the syntactic and semantic knowledge of code snippets from their token sequences and Abstract Syntax Trees (ASTs). They use the learned code representations as input to code summarization models, which can accordingly generate summaries describing source code. Traditional models traverse ASTs as sequences or split ASTs into paths as input. However, the former loses the structural properties of ASTs, and the latter destroys the overall structure of ASTs. Therefore, comprehensively capturing the structural features of ASTs in learning code representations for source code summarization remains a challenging problem to be solved. In this paper, we propose M2TS, a Multi-scale Multi-modal approach based on Transformer for source code Summarization. M2TS uses a multi-scale AST feature extraction method, which can extract the structures of ASTs more completely and accurately at multiple local and global levels. To complement missing semantic information in ASTs, we also obtain code token features, and further combine them with the extracted AST features using a cross modality fusion method that not only fuses the syntactic and contextual semantic information of source code, but also highlights the key features of each modality. We conduct experiments on two Java and one Python datasets, and the experimental results demonstrate that M2TS outperforms current state-of-the-art methods. We release our code at https://***/TranSMS/M2TS. Copyright © 2022, The Authors. All rights reserved.

关键词： computer programming languages

Direct Foundations for Compositional programming

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arXiv 2022年

作者： Fan, Andong Huang, Xuejing Xu, Han Sun, Yaozhu Oliveira, Bruno C.D.S. Zhejiang University Hangzhou China The University of Hong Kong Hong Kong Peking University Beijing China

The recently proposed CP language adopts Compositional programming: a new modular programming style that solves challenging problems such as the Expression Problem. CP is implemented on top of a polymorphic core language with disjoint intersection types called F+i. The semantics of F+i employs an elaboration to a target language and relies on a sophisticated proof technique to prove the coherence of the elaboration. Unfortunately, the proof technique is technically challenging and hard to scale to many common features, including recursion or impredicative polymorphism. Thus, the original formulation of F+i does not support the two later features, which creates a gap between theory and practice, since CP fundamentally relies on them. This paper presents a new formulation of F+i based on a type-directed operational semantics (TDOS). The TDOS approach was recently proposed to model the semantics of languages with disjoint intersection types (but without polymorphism). Our work shows that the TDOS approach can be extended to languages with disjoint polymorphism and model the full F+i calculus. Unlike the elaboration semantics, which gives the semantics to F+i indirectly via a target language, the TDOS approach gives a semantics to F+i directly. With a TDOS, there is no need for a coherence proof. Instead, we can simply prove that the semantics is deterministic. The proof of determinism only uses simple reasoning techniques, such as straightforward induction, and is able to handle problematic features such as recursion and impredicative polymorphism. This removes the gap between theory and practice and validates the original proofs of correctness for CP. We formalized the TDOS variant of the F+i calculus and all its proofs in the Coq proof assistant. © 2022, CC BY.

关键词： computer programming languages

Multi-view graph representation for programming language processing: An investigation into algorithm detection

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arXiv 2022年

作者： Long, Ting Xie, Yutong Chen, Xianyu Zhang, Weinan Cao, Qinxiang Yu, Yong Department Of Computer Science And Engineering Shanghai Jiao Tong University China School Of Information University Of Michigan Ann ArborMI United States

Program representation, which aims at converting program source code into vectors with automatically extracted features, is a fundamental problem in programming language processing (PLP). Recent work tries to represent programs with neural networks based on source code structures. However, such methods often focus on the syntax and consider only one single perspective of programs, limiting the representation power of models. This paper proposes a multiview graph (MVG) program representation method. MVG pays more attention to code semantics and simultaneously includes both data flow and control flow as multiple views. These views are then combined and processed by a graph neural network (GNN) to obtain a comprehensive program representation that covers various aspects. We thoroughly evaluate our proposed MVG approach in the context of algorithm detection, an important and challenging subfield of PLP. Specifically, we use a public dataset POJ-104 and also construct a new challenging dataset ALG-109 to test our method. In experiments, MVG outperforms previous methods significantly, demonstrating our model's strong capability of representing source code. Copyright © 2022, The Authors. All rights reserved.

关键词： computer programming languages

CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training

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arXiv 2022年

作者： Wang, Xin Wang, Yasheng Wan, Yao Wang, Jiawei Zhou, Pingyi Li, Li Wu, Hao Liu, Jin School of Computer Science Wuhan University China Huawei Noah’s Ark Lab. School of Computer Sci. & Tech. Huazhong University of Science and Technology China Faculty of Information Technology Monash University Australia School of Information Science and Engineering Yunnan University China

Recent years have witnessed increasing interest in code representation learning, which aims to represent the semantics of source code into distributed vectors. Currently, various works have been proposed to represent the complex semantics of source code from different views, including plain text, Abstract Syntax Tree (AST), and several kinds of code graphs (e.g., Control/Data Flow Graph). However, most of them only consider a single view of source code independently, ignoring the correspondences among different views. In this paper, we propose to integrate different views with the natural-language description of source code into a unified framework with Multi-View contrastive Pre-training, and name our model as CODE-MVP. Specifically, we first extract multiple code views using compiler tools, and learn the complementary information among them under a contrastive learning framework. Inspired by the type checking in compilation, we also design a fine-grained type inference objective in the pretraining. Experiments on three downstream tasks over five datasets demonstrate the superiority of CODE-MVP when compared with several state-of-the-art baselines. For example, we achieve 2.4/2.3/1.1 gain in terms of MRR/MAP/Accuracy metrics on natural language code retrieval, code similarity, and code defect detection tasks, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： computer programming languages

BINGO: Pinpointing concurrency bugs in go via binary analysis

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arXiv 2022年

作者： Zhong, Chongxin Zhao, Qidong Liu, Xu North Carolina State University United States

Golang (also known as Go for short) has become popular in building concurrency programs in distributed systems. As the unique features, Go employs lightweight Goroutines to support highly parallelism in user space. Moreover, Go leverages channels to enable explicit communication among threads. However, recent studies show that concurrency bugs are not uncommon in Go applications. Pinpointing these concurrency bugs in real Go applications is both important and challenging. Existing approaches are mostly based on compiler-aided static or dynamic analysis, which have two limitations. First, existing approaches require the availability and recompilation of the source code, which work well on testing rather than production environments with no source code available for both applications and external libraries. Second, existing approaches work on pure Go code bases only, not programs mixed with Go and other languages. To address these limitations, we develop BINGO, the first tool to identify concurrency bugs in Go applications via dynamic binary analysis. BINGO correlates binary execution with Go semantics and employs novel bug detection algorithms. BINGO is an end-to-end tool that is ready for deployment in the production environment with no modification on source code, compilers, and runtimes in the Go eco-system. Our experiments show that BINGO has a high coverage of concurrency bugs with no false positives. We are able to use BINGO to identify concurrency bugs in real applications with moderate overhead. © 2022, CC BY.

关键词： computer programming languages

Lang-n-Send: Processes That Send languages

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arXiv 2022年

作者： Cimini, Matteo University of Massachusetts Lowell LowellMA United States

We present LANG-N-SEND, a π-calculus that is equipped with language definitions. Processes can define languages in operational semantics, and use them to execute programs. Furthermore, processes can send and receive pieces of operational semantics through channels. We present a reduction semantics for LANG-N-SEND, and we offer examples that demonstrate some of the scenarios that LANG-N-SEND captures. © 2022, CC BY.

关键词： computer programming languages

MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning

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arXiv 2022年

作者： Pian, Weiguo Peng, Hanyu Tang, Xunzhu Sun, Tiezhu Tian, Haoye Habib, Andrew Klein, Jacques Bissyandé, Tegawendé F. SnT University of Luxembourg Luxembourg Baidu Inc. Beijing China CITADEL Université Virtuelle du Burkina Faso Burkina Faso

Representation learning of source code is essential for applying machine learning to software engineering tasks. Learning code representation from a multilingual source code dataset has been shown to be more effective than learning from single-language datasets separately, since more training data from multilingual dataset improves the model’s ability to extract language-agnostic information from source code. However, existing multilingual training overlooks the language-specific information which is crucial for modeling source code across different programming languages, while only focusing on learning a unified model with shared parameters among different languages for language-agnostic information modeling. To address this problem, we propose MetaTPTrans, a meta learning approach for multilingual code representation learning. MetaTPTrans generates different parameters for the feature extractor according to the specific programming language type of the input code snippet, enabling the model to learn both language-agnostic and language-specific information with dynamic parameters in the feature extractor. We conduct experiments on the code summarization and code completion tasks to verify the effectiveness of our approach. The results demonstrate the superiority of our approach with significant improvements on state-of-the-art baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： computer programming languages

Probabilistic programming languages for Modeling Autonomous Systems

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Probabilistic Programming Languages for Modeling Autonomous ...

International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Seyed Mahdi Shamsi Gian Pietro Farina Marco Gaboardi Nils Napp University at Buffalo Buffalo NY

ISBN: (数字)9781728164229

ISBN: (纸本)9781728164236

We present a robotic development framework called ROSPPL, which can accomplish many of the essential probabilistic tasks that comprise modern autonomous systems and is based on a general purpose probabilistic programming language (PPL). Benefiting from ROS integration, a short PPL program in our framework is capable of controlling a robotic system, estimating its current state online, as well as automatically calibrating parameters and detecting errors, simply through probabilistic model and policy specification. The advantage of our approach lies in its generality which makes it useful for quickly designing and prototyping of new robots. By directly modeling the interconnection of random variables, decoupled from the inference engine, our design benefits from robustness, re-usability, upgradability, and ease of specification. In this paper, we use a SDV as an example of a complex autonomous system, to show how different sub-components of such system could be implemented using a probabilistic programming language, in a way that the system is capable of reasoning about itself. Our set of use-cases include localization, mapping, fault detection, calibration, and planning.

关键词： Probabilistic programming languages Robotics SLAM Planning Error Detection Calibration Error detection computer programming languages supersonic low altitude missile robotics Autonomous Systems new robot Probability Calibration Planning Probabilistic Model PPL gene PRIORITY PROBLEM LIST Specifications