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State/Action-based Fairness Verification for Non-determinism

Chinese Journal of Electronics 2023年第4期20卷 603-606页

作者： XU Guangquan PANG Shanchen GAO Honghao XIAO Yingyuan LI Xiaohong FENG Zhiyong School of Computer Science and Technology Tianjin University Tianjin China Department of Computer Science and Technology Tsinghua University Beijing China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China School of Computer Engineering and Science Shanghai University Shanghai China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

Most of the existing work about fairness verification was carried out by checking the fairness based on state, action, or process respectively. It is hard to resolve the fairness issue for complex software systems, especially in life-critical systems and safety-critical systems. In this paper, we verify the fairness based on both state and action, which is realized mainly by introducing an extended Kripke structure and four kinds of fairness (weak global/local fairness and strong global/local fairness). The experimental results show the superiority of our method by the savings in memory (2.08%) and time consumption (26.45%).

关键词： Memory management software systems

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The Dataflow Runtime Environment of DFC 1

引用

21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020

作者： Zhang, Jing Li, Jinrong Du, Zheng Shu, Jiwu Luo, Qiuming NHPCC/Guangdong Key Laboratory of Popular HPC College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

In this paper, we introduce the DFC dataflow language and its runtime environment. DFC runtime library is in charge of constructing the DAG of the dataflow graph, firing the DFC tasks and the synchronizations between the tasks of successive passes. Basing on an elaborately implemented thread pool and queued Active Data, DFC runtime shows an ideal performance comparing with DSPatch. The experiment of a simple dataflow graph shows that DFC has better performance for the cases that the parallelism beneath the core number, while DSPatch shows a better scalability for the cases of the parallelism exceed the core number. As DFC is still a prototype language, it lacks the ability to construct the DAG dynamically, which leads to low efficiency when coding for a well-structured dataflow graph. © 2021, Springer Nature Switzerland AG.

关键词： Data flow analysis

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An Approach towards Merging Grammars 1

An Approach towards Merging Grammars

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1st Intermountain engineering, Technology and Computing, IETC 2020

作者： Griffith, Isaac D. Roberts, Rosetta Empirical Software Engineering Laboratory Informatics and Computer Science Idaho State University PocatelloID83208 United States

ISBN: (纸本)9781728142913

Introduction: Since the introduction of Island Grammars, they have been successfully used for a variety of tasks, including impact analysis, multi-lingual parsing, and source code identification. However, there has been no attempt to automate the generation of Island Grammars. Objective: This research considers the development of a method to automate the merging of Island Grammar components. The goal of this is to facilitate the development of an approach to automate the creation of Island Grammars. The result of this is the reduction in initial effort and maintenance effort required for Island Grammar engineering. Methods: We develop an automated approach to merge the components of grammars. To evaluate this approach, we conducted two experiments, each using a factorial design of five replications each. We randomly selected pairs of grammars from each of three size categories to assess the effects of the merging process on the maintenance effort and complexity of the generated grammars. Results: We found that in nearly all cases, the application of this merging approach reduces the maintenance effort and complexity of the grammars. Limitations: The primary limitation of this research is that this approach is currently limited to grammars written in the Antlr4 grammar format. Conclusions: This work presents the initial steps towards the automated construction of Island and Tolerant Grammars. We have shown that this approach to merging grammar components follows suit with the expectations of Island and Tolerant grammars (reduction in maintenance effort and complexity). © 2020 IEEE.

关键词： Merging

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The Compiler of DFC: A Source Code Converter that Transform the Dataflow Code to the Multi-threaded C Code 1

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21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020

作者： Du, Zheng Zhang, Jing Li, Jinrong Du, Haixin Shu, Jiwu Luo, Qiuming Department of Computer Science and Technology Tsinghua University Beijing China NHPCC/Guangdong Key Laboratory of Popular HPC College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

The working principle of DFC compiler is introduced in this article. DFC is a grammatical extension of standard C language, with special DF function which describe the dependence of computing DAG. DFC compiler, dfcc, is used to convert the DFC source codes to standard C codes with the assistance of multi-threaded library. The lexical rules and grammatical rules are used to setup the AST of DFC codes, and then it is converted to the AST of standard C without DF function nodes. The derived AST is printed into a text file, a normal C file, and finally is processed by GCC to obtain the executable file. The experiment gives some demonstration of that converting details, and the memory footprint is studied to show an ideal scalability. © 2021, Springer Nature Switzerland AG.

关键词： Program compilers

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Can Large Language Models Understand Intermediate Representations?

arXiv

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arXiv 2025年

作者： Jiang, Hailong Zhu, Jianfeng Wan, Yao Fang, Bo Zhang, Hongyu Jin, Ruoming Guan, Qiang Department of Computer Science Kent State University Kent United States School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Pacific Northwest National Laboratory Richland United States School of Big Data and Software Engineering Chongqing University Chongqing China

Intermediate Representations (IRs) are essential in compiler design and program analysis, yet their comprehension by Large Language Models (LLMs) remains underexplored. This paper presents a pioneering empirical study to investigate the capabilities of LLMs, including GPT-4, GPT-3, Gemma 2, LLaMA 3.1, and Code Llama, in understanding IRs. We analyze their performance across four tasks: Control Flow Graph (CFG) reconstruction, decompilation, code summarization, and execution reasoning. Our results indicate that while LLMs demonstrate competence in parsing IR syntax and recognizing high-level structures, they struggle with control flow reasoning, execution semantics, and loop handling. Specifically, they often misinterpret branching instructions, omit critical IR operations, and rely on heuristic-based reasoning, leading to errors in CFG reconstruction, IR decompilation, and execution reasoning. The study underscores the necessity for IR-specific enhancements in LLMs, recommending fine-tuning on structured IR datasets and integration of explicit control flow models to augment their comprehension and handling of IR-related tasks.1 © 2025, CC BY.

关键词： Semantics

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DSD-MatchingNet:Deformable sparse-to-dense feature matching for learning accurate correspondences

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Virtual Reality & Intelligent Hardware 2022年第5期4卷 432-443页

作者： Yicheng ZHAO Han ZHANG Ping LU Ping LI Enhua WU Bin SHENG Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China ZTE Corporation Shenzhen 518057China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518057China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China School of Design The Hong Kong Polytechnic UniversityHong Kong 999077China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

Background Exploring correspondences across multiview images is the basis of various computer vision ***,most existing methods have limited accuracy under challenging *** To learn more robust and accurate correspondences,we propose DSD-MatchingNet for local feature matching in this ***,we develop a deformable feature extraction module to obtain multilevel feature maps,which harvest contextual information from dynamic receptive *** dynamic receptive fields provided by the deformable convolution network ensure that our method obtains dense and robust ***,we utilize sparse-to-dense matching with symmetry of correspondence to implement accurate pixel-level matching,which enables our method to produce more accurate *** Experiments show that our proposed DSD-MatchingNet achieves a better performance on the image matching benchmark,as well as on the visual localization ***,our method achieved 91.3%mean matching accuracy on the HPatches dataset and 99.3%visual localization recalls on the Aachen Day-Night dataset.

关键词： Image matching Deformable convolution network Sparse-to-dense matching

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Code Prediction Based on Graph Embedding Model 16th

Code Prediction Based on Graph Embedding Model

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Yang, Kang Yu, Huiqun Fan, Guisheng Yang, Xingguang Chen, Liqiong Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing China Shanghai China Shanghai Engineering Research Center of Smart Energy Shanghai China Department of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai200235 China

ISBN: (纸本)9783030675394

Code prediction aims to accelerate the efficiency of programmer development. However, its prediction accuracy is still a great challenge. To facilitate the interpretability of the code prediction model and improve the accuracy of prediction. In this paper, the source code’s Abstract Syntax Tree (AST) is used to extract relevant structural paths between nodes and convert them into training graphs. The embedded model can convert the feature of the node sequence in the training graph into a vector that is convenient for quantization. We calculate the similarity between the candidate value and the parent node vector of the predicted path to obtain the predicted value. Experiments show that by using prediction data to increase the weight of related nodes in the graph, the model can extract more useful structural features, especially in Value prediction tasks. Adjusting the parameters embedded in the graph can improve the accuracy of the model. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Trees (mathematics)

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RAT: A Refactoring-Aware Traceability Model for Bug Localization

RAT: A Refactoring-Aware Traceability Model for Bug Localiza...

引用

International Conference on software engineering (ICSE)

作者： Feifei Niu Wesley K. G. Assunção LiGuo Huang Christoph Mayr-Dorn Jidong Ge Bin Luo Alexander Egyed State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science and Engineering Southern Methodist University Dallas Texas USA

A large number of bug reports are created during the evolution of a software system. Locating the source code files that need to be changed in order to fix these bugs is a challenging task. Information retrieval-based bug localization techniques do so by correlating bug reports with historical information about the source code (e.g., previously resolved bug reports, commit logs). These techniques have shown to be efficient and easy to use. However, one flaw that is nearly omnipresent in all these techniques is that they ignore code refactorings. Code refactorings are common during software system evolution, but from the perspective of typical version control systems, they break the code history. For example, a class when renamed then appears as two separate classes with separate histories. Obviously, this is a problem that affects any technique that leverages code history. This paper proposes a refactoring-aware traceability model to keep track of the code evolution history. With this model, we reconstruct the code history by analyzing the impact of code refactorings to correctly stitch together what would otherwise be a fragmented history. To demonstrate that a refactoring aware history is indeed beneficial, we investigated three widely adopted bug localization techniques that make use of code history, which are important components in existing approaches. Our evaluation on 11 open source projects shows that taking code refactorings into account significantly improves the results of these bug localization techniques without significant changes to the techniques themselves. The more refactorings are used in a project, the stronger the benefit we observed. Based on our findings, we believe that much of the state of the art leveraging code history should benefit from our work.

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A Systematic Literature Review on Large Language Models for Automated Program Repair

arXiv

引用

arXiv 2024年

作者： Zhang, Quanjun Fang, Chunrong Xie, Yang Ma, Yuxiang Sun, Weisong Yang, Yun Chen, Zhenyu State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing Technologies Swinburne University of Technology Australia

Automated Program Repair (APR) attempts to patch software bugs and reduce manual debugging efforts. Very recently, with the advances in Large Language Models (LLMs), a rapidly increasing number of APR techniques have been proposed, significantly facilitating software development and maintenance and demonstrating remarkable performance. However, due to ongoing explorations in the LLM-based APR field, it is challenging for researchers to understand the current achievements, challenges, and potential opportunities. This work provides the first systematic literature review to summarize the applications of LLMs in APR between 2020 and 2024. We analyze 127 relevant papers from LLMs, APR and their integration perspectives. First, we categorize existing popular LLMs that are applied to support APR and outline three types of utilization strategies for their deployment. Besides, we detail some specific repair scenarios that benefit from LLMs, e.g., semantic bugs and security vulnerabilities. Furthermore, we discuss several critical aspects of integrating LLMs into APR research, e.g., input forms and open science. Finally, we highlight a set of challenges remaining to be investigated and the potential guidelines for future research. Overall, our paper provides a systematic overview of the research landscape to the APR community, helping researchers gain a comprehensive understanding of achievements and promote future research. Our artifacts are publicly available at the GitHub repository: https://***/iSEngLab/AwesomeLLM4APR. © 2024, CC BY.

关键词： Repair

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SIMAC: A Semantic-Driven Integrated Multimodal Sensing And Communication Framework

arXiv

引用

arXiv 2025年

作者： Peng, Yubo Xiang, Luping Yang, Kun Jiang, Feibo Wang, Kezhi Wu, Dapeng Oliver State Key Laboratory of Novel Software Technology Nanjing University Nanjing China Suzhou China School of Information Science and Engineering Hunan Normal University Changsha China Department of Computer Science Brunel University London United Kingdom Department of Computer Science City University of Hong Kong Hong Kong

Traditional single-modality sensing faces limitations in accuracy and capability, and its decoupled implementation with communication systems increases latency in bandwidth-constrained environments. Additionally, single-task-oriented sensing systems fail to address users’ diverse demands. To overcome these challenges, we propose a semantic-driven integrated multimodal sensing and communication (SIMAC) framework. This framework leverages a joint source-channel coding architecture to achieve simultaneous sensing decoding and transmission of sensing results. Specifically, SIMAC first introduces a multimodal semantic fusion (MSF) network, which employs two extractors to extract semantic information from radar signals and images, respectively. MSF then applies cross-attention mechanisms to fuse these unimodal features and generate multimodal semantic representations. Secondly, we present a large language model (LLM)-based semantic encoder (LSE), where relevant communication parameters and multimodal semantics are mapped into a unified latent space and input to the LLM, enabling channel-adaptive semantic encoding. Thirdly, a task-oriented sensing semantic decoder (SSD) is proposed, in which different decoded heads are designed according to the specific needs of tasks. Simultaneously, a multi-task learning strategy is introduced to train the SIMAC framework, achieving diverse sensing services. Finally, experimental simulations demonstrate that the proposed framework achieves diverse sensing services and higher accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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