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AsyCo: an asymmetric dual-task co-training model for partial-label learning

science China(Information sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

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Deep learning-based software engineering: progress,challenges, and opportunities

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science China(Information sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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An infrastructure software perspective toward computation offloading between executable specifications and foundation models

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science China(Information sciences) 2025年第4期68卷 380-382页

作者： Dezhi RAN Mengzhou WU Yuan CAO Assaf MARRON David HAREL Tao XIE Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education School of Computer SciencePeking University School of Electronics Engineering and Computer Science Peking University Department of Computer Science and Applied Mathematics Weizmann Institute of Science

Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of software engineering theories and methodologies [2]. Instead of replacing existing software modules implemented by symbolic logic, incorporating FMs' capabilities to build software systems requires entirely new modules that leverage the unique capabilities of ***, while FMs excel at handling uncertainty, recognizing patterns, and processing unstructured data, we need new engineering theories that support the paradigm shift from explicitly programming and maintaining user-defined symbolic logic to creating rich, expressive requirements that FMs can accurately perceive and implement.

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Grey-box Adversarial Attack on Communication in Communicative Multi-agent Reinforcement Learning

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4679-4693页

作者： Ma, Xiao Li, Wu-Jun Nanjing University National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing210023 China

Effective communication is a necessary condition for intelligent agents to collaborate in multi-agent environments. Although increasing attention has been paid to communicative multi-agent reinforcement learning (CMARL), the vulnerability of the communication mechanism in CMARL has not been well investigated, especially when there exist malicious agents that send adversarial communication messages to other regular agents. Existing works about adversarial communication in CMARL focus on black-box attack where the attacker cannot access any model within the multi-agent system (MAS). However, a more practical attack is grey-box attack, where the attacker has access to the models of their controlled agents. To the best of our knowledge, no research has been conducted to investigate grey-box attacks on communication in CMARL. In this paper, we propose the first grey-box attack method on communication in CMARL, which is called victim-simulation based adversarial attack (VSAA). At each timestep, the attacker simulates a victim attacked by other regular agents' communication messages and generates adversarial perturbations on its received communication messages. The attacker then sends the aggregation of these perturbations to the regular agents through communication messages, which will induce non-optimal actions of the regular agents and subsequently degrade the performance of the MAS. Experimental results on multiple tasks show that VSAA can effectively degrade the performance of the MAS. The findings in this paper will make researchers be aware of the grey-box attack in CMARL. © 2005-2012 IEEE.

关键词： Adversarial machine learning

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GTE: learning code AST representation efficiently and effectively

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science China(Information sciences) 2025年第3期68卷 393-394页

作者： Yihao QIN Shangwen WANG Bo LIN Kang YANG Xiaoguang MAO College of Computer Science and Technology National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology

With the development of deep learning in recent years, code representation learning techniques have become the foundation of many software engineering tasks such as program classification [1] and defect detection. Earlier approaches treat the code as token sequences and use CNN, RNN, and the Transformer models to learn code representations.

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Clustered Reinforcement Learning

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Frontiers of computer science 2025年第4期19卷 43-57页

作者： Xiao MA Shen-Yi ZHAO Zhao-Heng YIN Wu-Jun LI National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing UniversityNanjing 210023China Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720-1770USA

Exploration strategy design is a challenging problem in reinforcement learning(RL),especially when the environment contains a large state space or sparse *** exploration,the agent tries to discover unexplored(novel)areas or high reward(quality)*** existing methods perform exploration by only utilizing the novelty of *** novelty and quality in the neighboring area of the current state have not been well utilized to simultaneously guide the agent’s *** address this problem,this paper proposes a novel RL framework,called clustered reinforcement learning(CRL),for efficient exploration in *** adopts clustering to divide the collected states into several clusters,based on which a bonus reward reflecting both novelty and quality in the neighboring area(cluster)of the current state is given to the *** leverages these bonus rewards to guide the agent to perform efficient ***,CRL can be combined with existing exploration strategies to improve their performance,as the bonus rewards employed by these existing exploration strategies solely capture the novelty of *** on four continuous control tasks and six hard-exploration Atari-2600 games show that our method can outperform other state-of-the-art methods to achieve the best performance.

关键词： deep reinforcement learning exploration count-based method clustering K-means

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Formalization of Android Activity-Fragment Multitasking Mechanism and Static Analysis of Mobile Apps

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Formal Aspects of Computing 2025年第2期37卷 1-86页

作者： He, Jinlong Wu, Zhilin Chen, Taolue Key Laboratory of System Software Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Computing and Mathematical Sciences Birkbeck University of London Northern Ireland London United Kingdom

The multitasking mechanism between activities and fragments plays a fundamental role in the Android operating system, which involves a wide range of features, including launch modes, intent flags, task affinities, and structured activities containing fragments. All of them are being widely used in Android apps, both open source and commercial ones. In this article, we present a formal semantics of the Android multitasking mechanism between activities and fragments, which accommodates all the important features and gives insofar the most comprehensive and accurate formalization. In particular, our semantics is formulated based on multi-stack systems, and fully captures the behavior of task stacks and activity stacks regarding fragments. Based on the semantics, we provide new static analysis algorithms, which are both multi-stack aware and fragment sensitive, thus achieve more precise static analysis for Android apps. We validate our approach by extensive experiments on both open source and commercial Android apps. The results highlight the benefits of the considering the semantics of the multitasking mechanism between activities and fragments in static analysis, and confirm the efficacy of our approach. © 2025 Copyright held by the owner/author(s).

关键词： Android multitasking mechanism activities and fragments formal semantics stack unboundedness static analysis

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Instruction selection optimization for VLIW architecture based on classification node merging

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Journal of Supercomputing 2025年第7期81卷 1-19页

作者： Liu, Fangjun Zhang, Huifu Hu, Yonghua Xie, Anxing Cao, Wei Mo, Shangfeng School of Computer Science and Engineering Hunan University of Science and Technology XiangTan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology XiangTan411201 China

Traditional instruction selection methods fail to fully exploit the very long instruction word (VLIW) architecture’s efficient scalar instructions. We propose an optimized instruction selection method based on classification node merging. This method abstracts scalar intermediate instructions into nodes and divides them into associated and similar ones according to operational semantics. Combined with the VLIW architecture’s instruction set features and guided by the instruction cost model, the nodes are merged and optimized to select efficient instructions. We design an instruction generation framework that swiftly converts the low level virtual machine intermediate representation into the instruction sequence of the target architecture. The experimental results show that, compared with the instruction sequence generated by M7002-cc, the instruction sequence generated with the solution proposed in this paper exhibits an average execution performance that is up to 54% higher on the FT-M7002 hardware platform. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Real time systems

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Synthesizing Invariants for Polynomial Programs by Semidefinite Programming

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ACM Transactions on Programming Languages and Systems 2025年第1期47卷 1-35页

作者： Wu, Hao Wang, Qiuye Xue, Bai Zhan, Naijun Zhi, Lihong Yang, Zhi-Hong State Key Lab. of Computer Sciences Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Fermat Labs Huawei Inc. Dongguan China Key Lab. of System Software and State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Key Laboratory of High Confidence Software Technology School of Computer Science Peking University Beijing China School of Mathematics and Statistics Central South University Changsha China

Constraint-solving-based program invariant synthesis takes a parametric invariant template and encodes the (inductive) invariant conditions into constraints. The problem of characterizing the set of all valid parameter assignments is referred to as the strong invariant synthesis problem, while the problem of finding a concrete valid parameter assignment is called the weak invariant synthesis problem. For both problems, the challenge lies in solving or reducing the encoded constraints, which are generally non-convex and lack efficient solvers. In this article, we propose two novel algorithms for synthesizing invariants of polynomial programs using semidefinite programming (SDP): (1) The Cluster algorithm targets the strong invariant synthesis problem for polynomial invariant templates. Leveraging robust optimization techniques, it solves a series of SDP relaxations and yields a sequence of increasingly precise under-approximations of the set of valid parameter assignments. We prove the algorithm’s soundness, convergence, and weak completeness under a specific robustness assumption on templates. Moreover, the outputs can simplify the weak invariant synthesis problem. (2) The Mask algorithm addresses the weak invariant synthesis problem in scenarios where the aforementioned robustness assumption does not hold, rendering the Cluster algorithm ineffective. It identifies a specific subclass of invariant templates, termed masked templates, involving parameterized polynomial equalities and known inequalities. By applying variable substitution, the algorithm transforms constraints into an equivalent form amenable to SDP relaxations. Both algorithms have been implemented and demonstrated superior performance compared to state-of-the-art methods in our empirical evaluation. © 2025 Copyright held by the owner/author(s).

关键词： Polynomial approximation

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Accurate 3D Wireless Charging

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4733-4746页

作者： Yang, Wei Lin, Chi Sun, Yu Dai, Haipeng Ren, Jiankang Wang, Lei Wu, Guowei Dalian University of Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology DUT-RU International School of Information Science and Engineering Dalian China Nanjing University State Key Laboratory for Novel Software Technology Jiangsu Nanjing210023 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China

Wireless Rechargeable Sensor Networks (WRSNs) have become an important research issue as they can overcome the energy bottleneck problem of wireless sensor networks. However, existing 2D charging methods suffer from significant errors in 3D scenarios, which leads to a huge gap between theoretical results and practical applications, hindering the widespread adoption of WRSNs. In this paper, we address the chargIng utility maximizatioN problem In 3D environmenT (INIT) and provide a general solution suitable for any type of transceiver antenna. Specifically, we first establish an accurate 3D charging model to quantify the received power of sensors in 3D environments. Secondly, we design an angle-distance discretization scheme to determine appropriate charging spots for the Mobile Charger (MC). Then, we transform the mobile charging problem into a submodular function maximization problem and propose an approximation algorithm with guaranteed performance to solve it. Finally, our method has been extensively evaluated through experiments and simulations and has demonstrated considerable advantages over other comparison algorithms in real-world 3D environments. On average, it has achieved an impressive 34.8% improvement in charging utility and a remarkable 56.1% reduction in the number of dead sensors. © 2002-2012 IEEE.

关键词： Charging stations

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