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arXiv 2023年

作者： Zhan, Qi Xia, Xin Hu, Xing Lo, David Li, Zhiyang Li, Shanping The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Software Engineering Application Technology Lab China Singapore Management University Singapore Zhejiang University Hangzhou China

During software development, vulnerabilities have posed a significant threat to users. Patches are the most effective way to combat vulnerabilities. In a large-scale software system, testing the presence of a security patch in every affected binary is crucial to ensure system security. Identifying whether a binary has been patched for a known vulnerability is challenging, as there may only be small differences between patched and vulnerable versions. Existing approaches mainly focus on detecting patches that are compiled in the same compiler options. However, it is common for developers to compile programs with very different compiler options in different situations, which causes inaccuracy for existing methods. In this paper, we propose a new approach named PS3, referring to precise patch presence test based on semantic-level symbolic signature. PS3 exploits symbolic emulation to extract signatures that are stable under different compiler options. Then PS3 can precisely test the presence of the patch by comparing the signatures between the reference and the target at semantic level. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 3,631 (CVE, binary) pairs of 62 recent CVEs in four C/C++ projects. The experimental results show that PS3 achieves scores of 0.82, 0.97, and 0.89 in terms of precision, recall, and F1 score, respectively. PS3 outperforms the state-of-the-art baselines by improving 33% in terms of F1 score and remains stable in different compiler options. © 2023, CC BY.

关键词： software design

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A Cross-Chain Protocol Based on Main-SubChain Architecture

A Cross-Chain Protocol Based on Main-SubChain Architecture

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Computer Science and Blockchain (CCSB), International Conference on

作者： Feng Zhang Yu Le Rong Wang Minfu Yuan Wei-Tek Tsai Yuansheng Dong China Mobile Information Security Management and Operation Center Beijing China Guangzhou Institute of Software Guangzhou China College of Computer and Data Science Fuzhou University Arizona State University Tempe AZ USA Huanggang Polytechnic College Huanggang China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

Addressing the current surge in blockchain applications and the increasingly significant throughput and scalability challenges of single-chain systems, we propose a cross-chain protocol based on a main-subchain architecture, utilizing a “ $1+\mathrm{N}$ ” model composed of a main blockchain and multiple sidechains. The main blockchain is a permissioned public blockchain that employs relay chain technology, combining the flexibility and scalability of public blockchains with the performance, security, and regulatory advantages of federated blockchains. It serves as the core of the architecture, managing and coordinating the operations of the sidechains. Sidechains maintain a high degree of independence, selecting consensus algorithms tailored to their specific business needs and processing transactions asynchronously across different chains. Cross-chain transactions are decomposed into multiple sub-transactions, which are independently processed and verified by the relevant sidechains and the committee blockchain. This architecture ensures the atomicity, consistency, and security of transactions while enhancing the system's parallel processing capability and overall performance. Finally, this paper demonstrates the superiority of the proposed scheme in terms of fund security, atomicity, consistency, continuity, and dynamics through theoretical analysis.

关键词： Protocols Scalability Computer architecture Consensus algorithm Parallel processing Throughput Blockchains security Surges Relays

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QUIC Performance Evaluation: From the Prespective of Internet Competing Traffic

QUIC Performance Evaluation: From the Prespective of Interne...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Yuehua Fan Jinquan Nie Zhiming Zhang Liang Shan Junyi Wu School of Software Jiangxi Normal University Nanchang China Jiangxi Provincial Engineering Research Center of Blockchain Data Security and Governance Nanchang China Management Science and Engineering Center Jiangxi Normal University Nanchang China

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

With the rise of the Industrial Internet and Industry 4.0, the Internet of Things (IoT) has been widely applied in various fields. Within IoT applications, HTTP 3.0 plays a crucial role and brings new innovations to many applications. As the underlying protocol of HTTP 3.0, the QUIC protocol will provide a reliable and efficient data transmission foundation for IoT applications based on the HTTP 3.0 protocol. Currently, numerous scholars both domestically and internationally are dedicated to the performance analysis of the QUIC protocol. However, existing studies on the QUIC protocol have rarely taken into consideration the influence of background traffic, despite its significant impact on the performance testing of network protocols. This paper aims to investigate the impact of background traffic on the performance of the QUIC protocol. We set up a network topology based on the actual proportion of TCP traffic and UDP traffic in the network and simulating or adding an appropriate ratio of TCP background traffic. Using latency and throughput as evaluation metrics, we will explore the effects of background traffic on the QUIC protocol and investigate the impact of different congestion control algorithms and buffer sizes on protocol performance. This research aims to provide references for future testing of the QUIC protocol.

关键词： Performance evaluation Technological innovation Network topology Throughput HTTP Performance analysis Fourth Industrial Revolution

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Ppt4j: Patch Presence Test for Java Binaries

Ppt4j: Patch Presence Test for Java Binaries

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International Conference on software Engineering (ICSE)

作者： Zhiyuan Pan Xing Hu Xin Xia Xian Zhan David Lo Xiaohu Yang The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei Hangzhou China The Hong Kong Polytechnic University Hong Kong China School of Computing and Information Systems Singapore Management University Singapore

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

The number of vulnerabilities reported in open source software has increased substantially in recent years. security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4j (Patch Presence Test forJava Binaries). Ppt4j is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4j, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4j achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4j on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor.

关键词： Java Semantics Feature extraction Libraries security software measurement data mining

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Research on Personalized Course Recommendation for Online MOOC Platforms Based on Emotion Recognition

Frontiers in Artificial Intelligence and Applications

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Frontiers in Artificial Intelligence and Applications 2025年 404卷 31-41页

作者： Li, Bing Hou, Yuqi Yang, Biao School of software Jiangxi Normal University Jiangxi Nanchang330022 China Research Centre for Management Science and Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Jiangxi Provincial Engineering Research Center of Blockchain Data Security and Governance Jiangxi Nanchang330022 China School of digital Industries Jiangxi Normal University Jiangxi Nanchang330022 China

In the era of smart education, online courses as the avant-garde force in the educational field are leading the way in innovating teaching methods. Although online learning platforms provide students with convenient channels for learning, issues such as course quality, personalized service, and learning motivation still exist. This study, based on China University MOOC, proposes a personalized online course recommendation method based on emotion recognition, aimed at deeply understanding students' emotional states to enhance the accuracy and personalization of course recommendations. Initially, this paper collected a dataset of course user comments from China University MOOC and built an emotional dictionary in the education domain to analyze users' emotional states. Combining emotion analysis, user characteristics, and course features, the SAFM and SDFM models were proposed, incorporating a negative sampling method to generate personalized course recommendations. The experiments prove that this method effectively enhances students' learning motivation and participation, offering new insights for the development of online education platforms. © 2025 The Authors.

关键词： data mining

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Multi-perspective temporal information fusion perception for next basket recommendation

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The Journal of Supercomputing 2025年第9期81卷 1-23页

作者： Li, Bing Yang, Biao Hou, Yuqi Wang, Xile Dong, Jiangtao Yang, Zhijian Research Centre for Management Science and Engineering Jiangxi Normal University Nanchang China Jiangxi Provincial Engineering Research Center of Blockchain Data Security and Governance Nanchang China School of Software Jiangxi Normal University Nanchang China School of Digital Industry Jiangxi Normal University Shangrao China School of Computer and Information Engineering Jiangxi Normal University Nanchang China

Next Basket Recommendation System aims to predict the series of items a user is likely to purchase next based on their historical interaction data. While existing next basket recommendation models focus on user-item relationships and latent temporal patterns, they often neglect the impact of actual temporal information in interaction data. To address this issue, this study proposes a novel framework that explores how multi-perspective temporal information fusion can enhance prediction accuracy. The framework consists of two key modules: the latent temporal information extraction module, which analyzes the item perspective to capture the evolution of user preferences; and the actual temporal information extraction module, which examines the time perspective to reveal the preference differences of users at different time points. By fusing multi-perspective temporal information, the framework can more comprehensively capture the dynamic changes in user interests, thus improving the accuracy of recommendations. Extensive experiments on four real-world datasets validate the effectiveness of the proposed framework and emphasize the importance of temporal information in next basket recommendation.

关键词： Information fusion

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Code Search Is All You Need? Improving Code Suggestions with Code Search

Code Search Is All You Need? Improving Code Suggestions with...

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International Conference on software Engineering (ICSE)

作者： Junkai Chen Xing Hu Zhenhao Li Cuiyun Gao Xin Xia David Lo School of Software Technology Zhejiang University Ningbo China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Concordia University Montreal Canada Harbin Institute of Technology Shenzhen China Zhejiang University Hangzhou China Singapore Management University Singapore

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Modern integrated development environments (IDEs) provide various automated code suggestion techniques (e.g., code completion and code generation) to help developers improve their efficiency. Such techniques may retrieve similar code snippets from the code base or leverage deep learning models to provide code suggestions. However, how to effectively enhance the code suggestions using code retrieval has not been systematically investigated. In this paper, we study and explore a retrieval-augmented framework for code suggestions. Specifically, our framework leverages different retrieval approaches and search strategies to search similar code snippets. Then the retrieved code is used to further enhance the performance of language models on code suggestions. We conduct experiments by integrating different language models into our framework and compare the results with their original models. We find that our framework noticeably improves the performance of both code completion and code generation by up to 53.8% and 130.8% in terms of BLEU-4, respectively. Our study highlights that integrating the retrieval process into code suggestions can improve the performance of code suggestions by a large margin.

关键词： Deep learning Codes Search problems software engineering

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DRLFcc: Deep Reinforcement Learning-empowered Congestion Control Mechanism for TCP Fast Recovery in High Loss Wireless Networks

DRLFcc: Deep Reinforcement Learning-empowered Congestion Con...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yuanlong Cao Jinquan Nie Yuehua Fan Xun Shao Gang Lei School of Software Jiangxi Normal University Nanchang China Jiangxi Provincial Engineering Research Center of Blockchain Data Security and Governance Nanchang China Management Science and Engineering Center Jiangxi Normal University Nanchang China Toyohashi University of Technology Toyohashi Japan

TCP is currently the most widely used Internet transmission protocol, which is extensively applied to applications on the Internet to enable reliable data transmission. The TCP congestion control algorithm has a significant performance impact on all applications that use the TCP. However, traditional TCP congestion control algorithms rely on fixed feedback mechanisms, which can be challenging to adapt to complex and changing network environments and application scenarios, resulting in network performance bottlenecks. To address this issue, we design a congestion control windowing solution, DRLFcc, which is based on deep reinforcement learning and the TCP fast recovery mechanism. DRLFcc has demonstrated the ability to facilitate real-time adaptation of the congestion window to dynamic changes in network conditions while incorporating fast recovery mechanisms, thereby effectively enhancing network throughput and improving data transmission capacity recovery in high-loss wireless networks. The DRLFcc algorithm is validated in NS-3, and experimental results show an average improvement of 196% in effective throughput and a 22.4% reduction in round trip time. Compared to traditional TCP congestion control algorithms, the DRLFcc algorithm demonstrates superior performance and robustness.

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Ppt4J: Patch Presence Test for Java Binaries

arXiv

引用

arXiv 2023年

作者： Pan, Zhiyuan Hu, Xing Xia, Xin Zhan, Xian Lo, David Yang, Xiaohu The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei Hangzhou China The Hong Kong Polytechnic University Hong Kong Hong Kong School of Computing and Information Systems Singapore Management University Singapore

The number of vulnerabilities reported in open source software has increased substantially in recent years. security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4J (Patch Presence Test for Java Binaries). Ppt4J is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4J, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4J achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4J on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor. © 2023, CC BY-NC-ND.

关键词： Open source software

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Modelling V&V Workflows to Improve Development Processes of software-intensive Systems

Modelling V&V Workflows to Improve Development Processes of ...

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International Conference on Dependable Systems and Networks Workshops (DSN-W)

作者： Thomas Bauer Wolfgang Herzner Bob Hruska Katia Di Blasio Zain Shahwar Virtual Engineering Department Fraunhofer IESE Kaiserslautern Germany Center for Digital Safety & Security (DSS) Austrian Institute of Technology (AIT) Vienna Austria Consultant Department LieberLieber Software GmbH Vienna Austria Automotive and Smart System Division Intecs Solutions S.p.A. Pisa Italy Data Analytics and AI Solutions PUMACY TECHNOLOGIES AG Berlin Germany

The rapid increase in the complexity of systems, especially due to the integration of subsystems from different domains into cyber-physical systems, presents unique challenges for efficient verification and validation (V&V) processes to meet all requirements and system properties. To address these challenges and enhance quality assurance processes, it is essential to document and analyze V&V workflows. This paper proposes VVML, a novel approach for easy modeling of V&V activities in different industrial domains with varying constraints, V&V methods, and tool chains. This approach includes a dedicated modeling notation and a supporting modelling tool, enabling the creation of reusable workflows assets, such as V&V activities and artifacts, which can be shared across workflows. The VVML approach has been applied to 10+ industrial use cases. This paper explains the basic principles behind VVML and shows an example of its application to an industrial use case.

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