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International Conference on software Engineering (ICSE)

作者： Yuchao Huang Junjie Wang Zhe Liu Yawen Wang Song Wang Chunyang Chen Yuanzhe Hu Qing Wang Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Intelligent Game Institute of Software CAS Science and Technology on Integrated Information System Laboratory Institute of Software CAS University of Chinese Academy of Sciences York University Toronto Canada Monash University Melbourne Australia

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Crash reports are vital for software maintenance since they allow the developers to be informed of the problems encountered in the mobile application. Before fixing, developers need to reproduce the crash, which is an extremely time-consuming and tedious task. Existing studies conducted the automatic crash reproduction with the natural language described reproducing steps. Yet we find a non-neglectable portion of crash reports only contain the stack trace when the crash occurs. Such stack-trace-only crashes merely reveal the last GUI page when the crash occurs, and lack step-by-step guidance. Developers tend to spend more effort in understanding the problem and reproducing the crash, and existing techniques cannot work on this, thus calling for a greater need for automatic support. This paper proposes an approach named CrashTranslator to automatically reproduce mobile application crashes directly from the stack trace. It accomplishes this by leveraging a pre-trained Large Language Model to predict the exploration steps for triggering the crash, and designing a reinforcement learning based technique to mitigate the inaccurate prediction and guide the search holistically. We evaluate CrashTranslator on 75 crash reports involving 58 popular Android apps, and it successfully reproduces 61.3% of the crashes, outperforming the state-of-the-art baselines by 109% to 206%. Besides, the average reproducing time is 68.7 seconds, out-performing the baselines by 302% to 1611%. We also evaluate the usefulness of CrashTranslator with promising results.

关键词： software maintenance Computer bugs Reinforcement learning Computer crashes Mobile applications Task analysis Optimization

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Non-profiled Deep-Learning-Based Power Analysis of the SM4 and DES Algorithms

引用

Chinese Journal of Electronics 2021年第3期30卷 500-507页

作者： MA Xiangliang LI Bing WANG Hong WU Di ZHANG Lizhen HUANG Kezhen DUAN Xiaoyi School of Integrated Circuits Tsinghua University National Research Center for Information Technology Security China Cybersecurity Review Technology and Certification Center Trusted Computing and Information Assurance Laboratory Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Department of Electronics and Information Engineering Beijing Electronic Science and Technology Institute

Power analysis methods are commonly used for evaluating the security of cryptographic *** are characteristically low-cost and display a high success rate and the ability to obtain important device information, e.g., keys. Given the current wide application of deep-learning technology, there is a growing tendency to incorporate power-analysis technology in *** study investigates non-profiled deep-learning-based power analysis. The labels used in this attack are uncertain,and the attack conditions required are greatly reduced. We choose the Recurrent neural network(RNN), multilayer perceptron, and convolutional neural network algorithms,which use the same network structure, to recover the keys for the SM4 software and DES hardware *** propose combining the RNN algorithm with power analysis, and validate the benefits experimentally. The experimental results show that they all successfully recover the correct key for the SM4 software implementation,although the RNN algorithm by itself achieves a better effect. This conclusion also applies to attacks on the DES hardware implementation but is limited to labels based on the bit model.

关键词： cryptography nonprofiled deep-learning-based power analysis cryptographic device security recurrent neural nets RNN algorithm deep learning (artificial intelligence) DES hardware implementation convolutional neural nets convolutional neural network algorithms SM4 software implementation multilayer perceptrons recurrent neural network multilayer perceptron

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information Theory-Guided Heuristic Progressive Multi-View Coding

arXiv

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arXiv 2023年

作者： Li, Jiangmeng Gao, Hang Qiang, Wenwen Zheng, Changwen Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Multi-view representation learning aims to capture comprehensive information from multiple views of a shared context. Recent works intuitively apply contrastive learning to different views in a pairwise manner, which is still scalable: view-specific noise is not filtered in learning view-shared representations;the fake negative pairs, where the negative terms are actually within the same class as the positive, and the real negative pairs are coequally treated;evenly measuring the similarities between terms might interfere with optimization. Importantly, few works study the theoretical framework of generalized self-supervised multi-view learning, especially for more than two views. To this end, we rethink the existing multi-view learning paradigm from the perspective of information theory and then propose a novel information theoretical framework for generalized multi-view learning. Guided by it, we build a multi-view coding method with a three-tier progressive architecture, namely information theory-guided heuristic Progressive Multi-view Coding (IPMC). In the distribution-tier, IPMC aligns the distribution between views to reduce view-specific noise. In the set-tier, IPMC constructs self-adjusted contrasting pools, which are adaptively modified by a view filter. Lastly, in the instance-tier, we adopt a designed unified loss to learn representations and reduce the gradient interference. Theoretically and empirically, we demonstrate the superiority of IPMC over state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： information theory

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information Theory-Guided Heuristic Progressive Multi-View Coding

SSRN

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SSRN 2023年

Multi-view representation learning aims to capture comprehensive information from multiple views of a shared context. Recent works intuitively apply contrastive learning to different views in a pairwise manner, which is still scalable: view-specific noise is not filtered in learning view-shared representations;the fake negative pairs, where the negative terms are actually within the same class as the positive, and the real negative pairs are coequally treated;evenly measuring the similarities between terms might interfere with optimization. Importantly, few works study the theoretical framework of generalized self-supervised multi-view learning, especially for more than two views. To this end, we rethink the existing multi-view learning paradigm from the perspective of information theory and then propose a novel information theoretical framework for generalized multi-view learning. Guided by it, we build a multi-view coding method with a three-tier progressive architecture, namely information theory-guided hierarchical Progressive Multi-view Coding (IPMC). In the distribution-tier, IPMC aligns the distribution between views to reduce view-specific noise. In the set-tier, IPMC constructs self-adjusted contrasting pools, which are adaptively modified by a view filter. Lastly, in the instance-tier, we adopt a designed unified loss to learn representations and reduce the gradient interference. Theoretically and empirically, we demonstrate the superiority of IPMC over state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： information theory

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Mimicking the Familiar: Dynamic Command Generation for information Theft Attacks in LLM Tool-Learning system

arXiv

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arXiv 2025年

作者： Jiang, Ziyou Li, Mingyang Yang, Guowei Wang, Junjie Huang, Yuekai Chang, Zhiyuan Wang, Qing State Key Laboratory of Intelligent Game Beijing China Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences China University of Queensland Australia

information theft attacks pose a significant risk to Large Language Model (LLM) tool-learning systems. Adversaries can inject malicious commands through compromised tools, manipulating LLMs to send sensitive information to these tools, which leads to potential privacy breaches. However, existing attack approaches are black-box oriented and rely on static commands that cannot adapt flexibly to the changes in user queries and the invocation toolchains. It makes malicious commands more likely to be detected by LLM and leads to attack failure. In this paper, we propose AUTOCMD, a dynamic attack command generation approach for information theft attacks in LLM tool-learning systems. Inspired by the concept of mimicking the familiar, AUTOCMD is capable of inferring the information utilized by upstream tools in the toolchain through learning on open-source systems and reinforcement with examples from the target systems, thereby generating more targeted commands for information theft. The evaluation results show that AUTOCMD outperforms the baselines with +13.2% ASRTheft, and can be generalized to new tool-learning systems to expose their information leakage risks. We also design four defense methods to effectively protect tool-learning systems from the attack. © 2025, CC BY.

关键词： information leakage

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Arfa: An Agile Regime-Based Floating-Point Optimization Approach for Rounding Errors

SSRN

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SSRN 2023年

作者： Hao, Jiangwei Xu, Jinchen Xia, Yuanyuan Zhang, Zuoyan Yang, Hongru Song, Guanghui Pang, Jianmin Zhao, Jie Information Engineering University Henan Zhengzhou450001 China Laboratory of Science and Technology on Integrated Information System Institute of Software Chinese Academy of Sciences Beijing Beijing100190 China

Recent approaches present the concept of regimes to partition the input domain D of a floating-point expression f_e, rewriting it in each regime where f_e shows larger errors such that the accuracy across D can be improved. These methods, however, fall short of both inferring regimes and searching rewrite *** this paper, we introduce a rewriting system called Arfa to address these issues. Given an f_e and its D, Arfa first seeks a rewrite substitution f_o that has lower errors across D and next plots the error distribution of f_o. The boundary line of this plot is then sketched, based on which an effective regime inference algorithm applicable to both numerically stable and unstable programs is implemented. For each regime where f_o should be rewritten, Arfa first normalizes f_o by considering the regime’s properties and next generates an incomplete set of f_o ’s rewrite candidates, which are prioritized according to the number of floating-point operators. Finally, Arfa selects the best rewrite substitution by empirically inspecting the errors of several top ranked rewrite candidates, with enhancing precision also considered as a complementary strategy. This search heuristic avoids the need to establish a cost model used by prior *** conduct experiments using 60 benchmarks, including 56 FPbench examples and four real-life programs. The results demonstrate that Arfa can reduce both maximum and average errors of the original expressions by 4.73 and 2.08 bits on average, and up to 33 and 16 bits, respectively;the experimental outcomes also show that Arfa exhibits lower errors, sometimes to a significant degree, than Herbie, PSAT (a global optimization framework that rewrites numerical programs via prioritized stochastic algebraic transformations), Daisy, and Regina. © 2023, The Authors. All rights reserved.

关键词： Errors

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GrammarT5: Grammar-integrated Pretrained Encoder-Decoder Neural Model for Code

GrammarT5: Grammar-Integrated Pretrained Encoder-Decoder Neu...

引用

International Conference on software Engineering (ICSE)

作者： Qihao Zhu Qingyuan Liang Zeyu Sun Yingfei Xiong Lu Zhang Shengyu Cheng Key Laboratory of HCST (PKU) MoE SCS Peking University Beijing China Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China ZTE Corporation Chengdu China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Pretrained models for code have exhibited promising performance across various code-related tasks, such as code summarization, code completion, code translation, and bug detection. However, despite their success, the majority of current models still represent code as a token sequence, which may not adequately capture the essence of the underlying code structure. In this work, we propose GrammarT5, a grammar-integrated encoder-decoder pretrained neural model for code. GrammarT5 employs a novel grammar-integrated representation, Tokenized Grammar Rule Sequence (TGRS), for code. TGRS is constructed based on the grammar rule sequence utilized in syntax-guided code generation and integrates syntax information with code tokens within an appropriate input length. Furthermore, we suggest at-taching language flags to help GrammarT5 differentiate between grammar rules of various programming languages. Finally, we in-troduce two novel pretraining tasks-Edge Prediction (EP), and Sub-Tree Prediction (STP) to learn syntactic information. Experiments were conducted on five code-related tasks using eleven datasets, demonstrating that GrammarT5 achieves state-of-the-art (SOTA) performance on most tasks in comparison to models of the same scale. Additionally, the paper illustrates that the proposed pretraining tasks and language flags can enhance GrammarT5 to better capture the syntax and semantics of code.

关键词： Computer languages Codes Semantics Syntactics Tagging Predictive models Transformers

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Molecular Graph Representation Learning via Structural Similarity information

arXiv

引用

arXiv 2024年

作者： Yao, Chengyu Huang, Hong Gao, Hang Wu, Fengge Chen, Haiming Zhao, Junsuo University of Chinese Academy of Sciences Beijing100081 China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing100081 China Key Laboratory of System Software State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China

Graph Neural Networks (GNNs) have been widely employed for feature representation learning in molecular graphs. Therefore, it is crucial to enhance the expressiveness of feature representation to ensure the effectiveness of GNNs. However, a significant portion of current research primarily focuses on the structural features within individual molecules, often overlooking the structural similarity between molecules, which is a crucial aspect encapsulating rich information on the relationship between molecular properties and structural characteristics. Thus, these approaches fail to capture the rich semantic information at the molecular structure level. To bridge this gap, we introduce the Molecular Structural Similarity Motif GNN (MSSM-GNN), a novel molecular graph representation learning method that can capture structural similarity information among molecules from a global perspective. In particular, we propose a specially designed graph that leverages graph kernel algorithms to represent the similarity between molecules quantitatively. Subsequently, we employ GNNs to learn feature representations from molecular graphs, aiming to enhance the accuracy of property prediction by incorporating additional molecular representation information. Finally, through a series of experiments conducted on both small-scale and large-scale molecular datasets, we demonstrate that our model consistently outperforms eleven state-of-the-art baselines. The codes are available at https://***/yaoyao-yaoyao-cell/MSSM-GNN. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration

arXiv

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arXiv 2024年

作者： Sun, Chuxiong Zang, Zehua Li, Jiabao Li, Jiangmeng Xu, Xiao Wang, Rui Zheng, Changwen Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences China State Key Laboratory of Intelligent Game China University of Chinese Academy of Sciences China School of Automation and Electrical Engineering University of Science and Technology Beijing China

Communication stands as a potent mechanism to harmonize the behaviors of multiple agents. However, existing works primarily concentrate on broadcast communication, which not only lacks practicality, but also leads to information redundancy. This surplus, one-fits-all information could adversely impact the communication efficiency. Furthermore, existing works often resort to basic mechanisms to integrate observed and received information, impairing the learning process. To tackle these difficulties, we propose Targeted and Trusted Multi-Agent Communication (T2MAC), a straightforward yet effective method that enables agents to learn selective engagement and evidence-driven integration. With T2MAC, agents have the capability to craft individualized messages, pinpoint ideal communication windows, and engage with reliable partners, thereby refining communication efficiency. Following the reception of messages, the agents integrate information observed and received from different sources at an evidence level. This process enables agents to collectively use evidence garnered from multiple perspectives, fostering trusted and cooperative behaviors. We evaluate our method on a diverse set of cooperative multi-agent tasks, with varying difficulties, involving different scales and ranging from Hallway, MPE to SMAC. The experiments indicate that the proposed model not only surpasses the state-of-the-art methods in terms of cooperative performance and communication efficiency, but also exhibits impressive generalization. Copyright © 2024, The Authors. All rights reserved.

关键词： Behavioral research

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Image Computation for Quantum Transition systems

Image Computation for Quantum Transition Systems

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Design, Automation and Test in Europe Conference and Exhibition

作者： Xin Hong Dingchao Gao Sanjiang Li Shenggang Ying Mingsheng Ying Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Centre for Quantum Software and Information University of Technology Sydney Sydney Australia

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

With the rapid progress in quantum hardware and software, the need for verification of quantum systems becomes increasingly crucial. While model checking is a dominant and very successful technique for verifying classical systems, its application to quantum systems is still an underdeveloped research area. This paper advances the development of model checking quantum systems by providing efficient image computation algorithms for quantum transition systems, which play a fundamental role in model checking. In our approach, we represent quantum circuits as tensor networks and design algorithms by leveraging the properties of tensor networks and tensor decision diagrams. Our experiments demonstrate that our contraction partition-based algorithm can greatly improve the efficiency of image computation for quantum transition systems.

关键词： Quantum system Tensors Europe Model checking software Hardware Partitioning algorithms Computational efficiency Quantum circuit

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