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A Comprehensive Study of OOP-Related Bugs in C++ Compilers

IEEE Transactions on Software Engineering 2025年 1-21页

作者： Wang, Bo Chen, Chong Chen, Junjie Xu, Bowen Ye, Chen Lin, Youfang Dong, Guoliang Sun, Jun Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence China Tianjin University College of Intelligence and Computing Tianjin300350 China North Carolina State University Department of Computer Science NC27695 United States Singapore Management University School of Computing and Information Systems Singapore178902 Singapore

Modern C++, a programming language characterized by its extensive use of object-oriented programming (OOP) features, is widely used for system programming. However, C++ compilers often struggle to correctly handle these sophisticated OOP features, resulting in numerous high-profile compiler bugs that can lead to crashes or miscompilation. Despite the significance of OOP-related bugs, existing studies largely overlook OOP features, hindering their ability to discover such bugs. To assist both compiler fuzzer designers and compiler developers, we conduct a comprehensive study of the compiler bugs caused by incorrectly handling C++ OOP-related features. First, we systematically extract 788 OOP-related C++ compiler bugs from GCC and LLVM. Second, derived from the core concepts of OOP and C++, we manually identified a two-level taxonomy of the OOP-related features leading to compiler bugs, which consists of 6 primary categories (e.g., Abstraction & Encapsulation, Inheritance, and Runtime Polymorphism), along with 17 secondary categories (e.g., Constructors & Destructors and Multiple Inheritance). Third, we systematically analyze the root causes, symptoms, fixes, options, and C++ standard versions of these bugs. Our analysis yields 13 key findings, highlighting that features related to the construction and destruction of objects lead to the highest number of bugs, crashes are the most frequent symptom, and while the average time from bug introduction to discovery is 1856 days, fixing the bug once discovered takes only 174 days on average. Additionally, more than half of the bugs can be triggered without any compiler options. These findings offer valuable insights not only for developing new compiler testing approaches but also for improving language design and compiler engineering. Inspired by these findings, we developed a proof-of-concept compiler fuzzer OOPFuzz, specifically targeting OOP-related bugs in C++ compilers. We applied it against the newest release versions

关键词： C++ (programming language)

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RViT: Robust Fusion Vision Transformer with Variational Hierarchical Denoising Process for Image Classification

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Guidance, Navigation and Control 2024年第3期4卷 191-217页

作者： Zhenghong Lin Yuze Wu Jiawei Chen Shiping Wang College of Computer and Data Science Fuzhou UniversityFuzhou 350108P.R.China Key Laboratory of Intelligent Metro Fujian Province UniversityFuzhou 350108P.R.China

Transformers designed for natural language processing have originally been explored for computer vision in recent research. Various Vision Transformers(ViTs) play an increasingly important role in the field of image tasks such as computer vision, multimodal fusion and multimedia analysis. However, to obtain promising performance, most existing ViTs usually rely on artificially filtered high-quality images, which may suffer from inherent noise ***, such well-constructed images are not always available in every situation. To this end,we propose a Robust ViT(RViT) to focus on the relevant and robust representation learning for image classification tasks. Specifically, we first develop a novel Denoising VTUnet module,where we conceptualize the nonrobust noise as the uncertainty under the variational ***, we design a fusion transformer backbone with a tailored fusion attention mechanism to perform image classification based on the extracted robust representations effectively. To demonstrate the superiority of our model, the compared experiments are conducted on several popular datasets. Benefiting from the sequence regularity of the Transformer and captured robust feature,the proposed method exceeds compared Transformer-based models with superior performance in visual tasks.

关键词： Image classification vision transformer robust representation learning variational inference fusion attention

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RTDU: Interpretable Region-Aware Transformer-Based Deep Unfolding Network for Pan-Sharpening

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12897-12914页

作者： Wang, Shuo Yuan, Genji Li, Jinjiang Shandong Technology and Business University School of Computer Science and Technology Yantai264005 China Shandong Technology and Business University Shandong Key Laboratory of Lightweight Intelligent Computing and Visualization for Digital Economy Yantai264005 China

Pan-sharpening is a fundamental and critical image processing method in remote sensing imaging. This technique reconstructs high-resolution multispectral (HRMS) images by leveraging the spectral information of low-resolution multispectral (LRMS) images and the spatial features of panchromatic (PAN) images (PAN). In recent years, various DL-based methods have emerged, achieving remarkable results. For instance, the vision transformer (ViT) has been applied in image processing, showing excellent performance in global information modeling, but still losing significant local information. To better preserve local details and integrate them with global information, we propose a region-aware transformer for the deep unfolding network for pan-sharpening. Specifically, we propose three optimization problems for pan-sharpening and design a region-aware transformer block (RA-Block) within the unfolding algorithm framework to model both global and local information for the proposed optimization problems, redefining the bottleneck. Additionally, we designed a triple-dimensional information perception (TDIP) module to address feature residuals among complementary channels. We integrate these components into the optimization framework, forming the unfolding network. All parameters in RTDU are adaptively learnable, providing our network with strong interpretability. We conducted experiments on the QuickBird (QB) and WorldView-2 (WV2) datasets. Through qualitative analysis and quantitative comparisons, we demonstrate the superiority of our proposed method. Ablation experiments further validate the effectiveness of our designed modules. © 2008-2012 IEEE.

关键词： Photointerpretation

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Evolutionary Multi-Objective Neural Architecture Search for Generalized Cognitive Diagnosis Models 5

Evolutionary Multi-Objective Neural Architecture Search for ...

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5th International Conference on data-Driven Optimization of Complex Systems, DOCS 2023

作者： Yang, Shangshang Zhen, Cheng Tian, Ye Ma, Haiping Liu, Yuanchao Zhang, Panpan Zhang, Xingyi School of Artificial Intelligence Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Automation for Process Industries Northeastern University State Key Laboratory of Synthetical Shenyang China

ISBN: (纸本)9798350393521

Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. Then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures. © 2023 IEEE.

关键词： Multiobjective optimization

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An Entity Alignment Algorithm Based on Ontology Enhancement and Multi-Feature Fusion 6

An Entity Alignment Algorithm Based on Ontology Enhancement ...

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6th International Conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Song, Hao Lei, Yuxia Zi, Kangli Quan, Fuyuan Zhang, Tianhang Li, Qi School of Computer Science Qufu Normal University Shandong Rizhao China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing China

ISBN: (纸本)9798331541750

Entity alignment plays a crucial role in the integration of knowledge graphs. However, current knowledge graphs often suffer from inconsistent construction standards, structural heterogeneity, and imprecise semantic representation of entities, which can lead to suboptimal embedding of structural information. Moreover, most existing entity alignment methods rely heavily on structural information, which limits alignment efficiency. To address these challenges, this paper presents Onto-IMF, an ontology-enhanced entity alignment method based on multi-feature fusion. This approach aims to enhance the semantic representation of entities by incorporating ontology information, thereby reducing structural discrepancies among heterogeneous graphs and improving the embedding of structural information. The multi-feature fusion entity alignment leverages diverse semantic features, including relations, attributes, and entity names, to achieve more accurate alignment. Experimental results on various DBP15K subsets indicate that the proposed method achieves Hits@l scores of 86.6%, 93.0%, and 97.7%, respectively, representing an average improvement of 7.9% over the best-performing baseline models. © 2024 IEEE.

关键词： Knowledge graph

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Joint Task Offloading and Service Caching for Vehicular Edge computing with Energy Constraints 16

Joint Task Offloading and Service Caching for Vehicular Edge...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Gao, Yifan Zhang, Haixia Liu, Qianqian Deng, Yiqin School of Control Science and Engineering Shandong University Jinan250061 China Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Jinan250061 China

ISBN: (纸本)9798350390643

Vehicular edge computing (VEC) enables vehicles to offload their tasks to idle vehicles for processing. To process tasks, the needed service model should be stored ahead. Considering the storage of vehicles is limited and downloading the service model upon tasks causes repeated data transmission, it is necessary to determine which service models should be cached and which tasks should be offloaded. For vehicles, the task completion time is crucial due to their mobility nature. Therefore, balancing the completion time and energy consumption for vehicles with limited battery capacity in highly dynamic system remains a significant challenge. To address this, we jointly designed task offloading and service caching scheme to minimize the tasks completion time in a cache-assisted VEC scenario while satisfying the long-term average energy constraints. We adopt Lyapunov optimization and Deep reinforcement learning (D RL) based Task offloading and Service caching (LDTS) to solve the proposed problem. Specifically, the Lyapunov optimization is used to tackle the issues of long-term energy consumption constraints by transforming the original problem into per-slot optimization problems that can be resolved using a model-free DRL method. Simulation results demonstrate that the LDTS method can minimize the completion time while satisfying the constraints of the long-term energy consumption budget. The results indicate that the LDTS method outperforms other benchmark methods. © 2024 IEEE.

关键词： Budget control

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A power optimization approach for mixed polarity Reed–Muller logic circuits based on multi-strategy fusion memetic algorithm

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Frontiers of Information Technology & Electronic Engineering 2025年第3期26卷 415-426页

作者： Mengyu ZHANG Zhenxue HE Yijin WANG Xiaojun ZHAO Xiaodan ZHANG Limin XIAO Xiang WANG Intelligent Agricultural Equipment Research Institute Hebei Agricultural UniversityBaoding 071001China Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural UniversityBaoding 071001China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Electronic Information Engineering Beihang UniversityBeijing 100191China

The power optimization of mixed polarity Reed–Muller(MPRM)logic circuits is a classic combinatorial optimization *** optimization approaches often suffer from slow convergence and a propensity to converge to local optima,limiting their effectiveness in achieving optimal power ***,we propose a novel multi-strategy fusion memetic algorithm(MFMA).MFMA integrates global exploration via the chimp optimization algorithm with local exploration using the coati optimization algorithm based on the optimal position learning and adaptive weight factor(COA-OLA),complemented by population management through truncation ***,leveraging MFMA,we propose a power optimization approach for MPRM logic circuits that searches for the best polarity configuration to minimize circuit *** results based on Microelectronics Center of North Carolina(MCNC)benchmark circuits demonstrate significant improvements over existing power optimization *** achieves a maximum power saving rate of 72.30%and an average optimization rate of 43.37%;it searches for solutions faster and with higher quality,validating its effectiveness and superiority in power optimization.

关键词： Power optimization Multi-strategy fusion memetic algorithm(MFMA) Mixed polarity Reed-Muller(MPRM) Combinatorial optimization problem

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Asymptotically Optimal Sequence Sets With Low/Zero Ambiguity Zone Properties

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IEEE Transactions on Information Theory 2025年第6期71卷 4785-4796页

作者： Tian, Liying Song, Xiaoshi Liu, Zilong Li, Yubo Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China University of Essex School of Computer Science and Electronics Engineering ColchesterCO4 3SQ United Kingdom Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China

Sequences with low/zero ambiguity zone (LAZ/ZAZ) properties are useful in modern communication and radar systems operating over mobile environments. This paper first presents a new family of ZAZ sequence sets motivated by the "modulating" zero correlation zone (ZCZ) sequences which were first proposed by Popovic and Mauritz. We then introduce a second family of ZAZ sequence sets with comb-like spectrum, whereby the local Doppler resilience is guaranteed by their inherent spectral nulls in the frequency domain. Finally, LAZ sequence sets are obtained by exploiting their connection with a novel class of mapping functions. These proposed unimodular ZAZ and LAZ sequence sets are cyclically distinct and asymptotically optimal with respect to the existing theoretical bounds on ambiguity functions. © 1963-2012 IEEE.

关键词： Frequency domain analysis

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Dual-Scale Cellular Traffic Prediction Based on FastDTW for Graph Neural Network 16

Dual-Scale Cellular Traffic Prediction Based on FastDTW for ...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Jia, Junrong Zhang, Haixia Zhou, Xiaotian School of Control Science and Engineering Shandong University Jinan250061 China Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Jinan250061 China

ISBN: (纸本)9798350390643

Cellular traffic prediction is crucial to the network efficiency as it provides prior knowledge to guide the wireless resource allocation. The artificial intelligence (AI) based traffic prediction can provide better prediction performance and has drawn increasing interests. Nevertheless, most existing works utilized Euclidean structure data, which can provide traffic prediction to only certain grid area rather than specific spot, such as each base station (BS). To give more precise BS level prediction, in this work we propose a dual-scale spatiotemporal prediction framework based on fast dynamic time warping (FastDTW) algorithm. The proposed framework can capture both the overall neighborhood relationship of historical traffic and the local neighborhood relationship graph to improve the prediction accuracy. More importantly, due to the utilization of FastDTW, it solves the disadvantage of the high complexity of the dynamic time warping (DTW) algorithm and completes the prediction at the BS level more efficiently. Simulation results confirms the generality and performance of the proposed method, which outperforms the benchmark algorithms. © 2024 IEEE.

关键词： Prediction models

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A novel deep domain-adversarial method based on DRN-DATL for rolling bearing 43

A novel deep domain-adversarial method based on DRN-DATL for...

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43rd Chinese Control Conference, CCC 2024

作者： Li, Zhuorui Wang, Xiaodong Ma, Bao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650500 China

ISBN: (纸本)9789887581581

In recent years, a variety of rolling bearing fault diagnosis methods based on deep learning has become an emerging research orientation. However, there is still a gap between the existing diagnostic model and the practical application, i.e., 1) due to the variability of working conditions, the domain shift problem between source domain and the target domain have led to the obvious degradation of the diagnostic performance. 2) It is time-consuming and expensive to label a mass of monitoring data in practical situation. To address this problem, a domain-adversarial transfer learning method based on deep residual network (DRN-DATL) for rolling bearing fault diagnosis is proposed. Firstly, DRN is designed to extract features from raw vibration signal, which can avoid gradient problems. Then, the APReLU activation function is adopted to improved diagnostic accuracy under different working conditions. Meanwhile, domain-adversarial training is utilized to minimize distribution differences between source domain and target domain. Finally, seven comparison methods are selected for systematic analysis, and the results show that the proposed method can effectively improve the overall performance of the diagnosis model, which has achieved higher classification accuracy. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Adversarial machine learning

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