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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Chaochao Shen Wenhua Yang Minxue Pan Yu Zhou College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key Laboratory for Novel Software Technology Nanjing University China

In the realm of collaborative software development, version control systems (VCS) like Git play an indispensable role, enabling concurrent development and facilitating seamless integration of disparate code contributions. Despite these benefits, merge conflicts resulting from simultaneous changes to identical code lines often pose significant challenges to the integration process. Addressing this challenge, our paper introduces a novel two-stage approach, termed as CHATMERGE, for resolving Git merge conflicts. CHATMERGE pioneers a unique strategy that employs machine learning to initially predict resolution strategies, and subsequently leverages a large language model, ChatGPT, to create resolutions for conflicts that necessitate complex resolution strategies. A series of comprehensive experiments validate CHATMERGE’s efficacy, demonstrating its impressive alignment with historical manual resolutions and its superior performance relative to existing, publicly accessible tools. The paper further explores the influence of various classification algorithms and the prompt construction process for ChatGPT, providing further insights into the merge conflict resolution process. Moreover, to foster continued advancements in this area, CHATMERGE, along with its associated training and testing datasets, is made publicly available, offering a valuable resource for both developers and researchers. This work, therefore, provides both an innovative solution to merge conflict resolution and a strong foundation for future explorations in this domain.

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An efficient split learning framework for recurrent neural network in mobile edge environment 22

An efficient split learning framework for recurrent neural n...

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2022 Conference on Research in Adaptive and Convergent Systems, RACS 2022

作者： Zhou, Wenxuan Qu, Zhihao Zhao, Yanchao Tang, Bin Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources and School of Computer and Information Hohai University Jiangsu Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China

ISBN: (纸本)9781450393980

Split learning is an emerging distributed machine learning paradigm that exhibits great advantages in privacy protection and training efficiency. Most previous studies pay attention to client-server based split learning on Convolutional Neural networks (CNNs), and little effort has been spent on investigating the Recurrent Neural Networks (RNNs) oriented split learning. To fill in this gap, we propose a multi-hop split learning algorithm for RNN in mobile edge computing, namely M-SLRNN. The core idea is to efficiently orchestrate resource-constrained mobile devices to collaboratively train a large-size model. We formulate the optimization problem with the objective of minimizing the total training time and theoretically analyze its hardness. Then, we propose an efficient algorithm that conditionally has an approximation ratio of 2. Extensive experiments on both image and text classification tasks demonstrate the effectiveness and the efficiency of the proposed algorithm. © 2022 ACM.

关键词： Recurrent neural networks

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CADEC: A Combinatorial Auction for Dynamic Distributed DNN Inference Scheduling in Edge-Cloud Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Xu, Xiaolong Hu, Yuhao Cui, Guangming Qi, Lianyong Dou, Wanchun Nanjing University of Information Science and Technology School of Software Nanjing210044 China Nanjing University of Information Science and Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software China Nanjing University State Key Laboratory for Novel Software Technology China College of Computer science and technology Qingdao China Georgia State University Department of Computer Science United States

Deep Neural Network (DNN) Inference, as a key enabler of intelligent applications, is often computation-intensive and latency-sensitive. Combining the advantages of cloud computing (abundant computing resources) and edge computing (fast transmission), edge-cloud collaborative DNN inference is a powerful solution to these problems. However, in edge-cloud networks with heterogeneous resources, how to obtain reasonable decisions on server selection, model partition and resource allocation for efficient distributed DNN inference is a hard challenge. Furthermore, it is non-trivial to design suitable resource prices to maximize the social welfare. These challenges even escalate in dynamic edge-cloud networks where decisions should be generated as soon as each user arrives without future information. Therefore, we design a combinatorial auction for dynamic distributed DNN inference scheduling, named CADEC. CADEC first constructs a bid set for each user based on convex optimization theory for optimal solution searching. Next, prices of resources in the edge-cloud network are adjusted according to changes in supply-demand relationship, and whether to admit the request of each user is decided. Finally, the dynamic distributed inference scheduling decisions are generated through the primal-dual algorithm to maximize the social welfare. Theoretical analysis shows the good competitive ratio and polynomial time complexity of CADEC. Results of simulation experiments present that CADEC improves social welfare by up to 46% compared with state-of-the-art distributed DNN inference schemes. © 2002-2012 IEEE.

关键词： Convex optimization

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Integrating Dual Prototypes for Task-Wise Adaption in Pre-Trained Model-Based Class-Incremental Learning

arXiv

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arXiv 2024年

作者： Xu, Zhiming Yang, Suorong Xu, Baile Zhao, Jian Shen, Furao National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China Department of Computer Science and Technology Nanjing University China

Class-incremental learning (CIL) aims to acquire new classes while conserving historical knowledge incrementally. Despite existing pre-trained model (PTM) based methods performing excellently in CIL, it is better to fine-tune them on downstream incremental tasks with massive patterns unknown to PTMs. However, using task streams for fine-tuning could lead to catastrophic forgetting that will erase the knowledge in PTMs. This paper proposes the Dual Prototype network for Task-wise Adaption (DPTA) of PTM-based CIL. For each incremental learning task, a task-wise adapter module is built to fine-tune the PTM, where the center-adapt loss forces the representation to be more centrally clustered and class separable. The dual prototype network improves the prediction process by enabling test-time adapter selection, where the raw prototypes deduce several possible task indexes of test samples to select suitable adapter modules for PTM, and the augmented prototypes that could separate highly correlated classes are utilized to determine the final result. Experiments on several benchmark datasets demonstrate the state-of-the-art performance of DPTA. The code is submitted in supplementary materials and will be open-sourced after the paper is published. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Dynamical Trust Construction Schema with Fuzzy Decision in P2P Systems

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Chinese Journal of Electronics 2023年第3期18卷 417-421页

作者： Yang Wang Ruchuan Wang Zhijie Han College of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China Department of Computer Anhui Normal University Wuhu China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

The security problem for Peer-to-peer (P2P) systems has been gradually recognized as an important topic of P2P applications. Among existing solution for P2P security, how to establish trust relationship among peers is a key problem in demand. This paper proposes a novel dynamical trust construction schema based on fuzzy decision and Extended automated trust negotiation (EATN). The presented approach adopts fuzzy trust graph to describe the trust information in the P2P systems. Trust relationship among peers can be constructed by the combination of the fuzzy trust graph, fuzzy path search algorithm and EATN. The relevant application case indicates the schema is feasible. The simulations also show that the presented schema can improve the interactive success ratio by enhancing the trust relationship in the P2P systems.

关键词： Simulation Heuristic algorithms Education Telecommunication traffic Peer-to-peer computing Security Trust management

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Attention Is All You Need for LLM-based Code Vulnerability Localization

arXiv

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arXiv 2024年

作者： Li, Yue Li, Xiao Wu, Hao Zhang, Yue Cheng, Xiuzhen Zhong, Sheng Xu, Fengyuan National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science Drexel University Philadelphia United States School of Computer Science and Technology Shandong University Qingdao China

The rapid expansion of software systems and the growing number of reported vulnerabilities have emphasized the importance of accurately identifying vulnerable code segments. Traditional methods for vulnerability localization, such as manual code audits or rule-based tools, are often time-consuming and limited in scope, typically focusing on specific programming languages or types of vulnerabilities. In recent years, the introduction of large language models (LLMs) such as GPT and LLaMA has opened new possibilities for automating vulnerability detection. However, while LLMs show promise in this area, they face challenges, particularly in maintaining accuracy over longer code contexts. This paper introduces LOVA, a novel framework leveraging the self-attention mechanisms inherent in LLMs to enhance vulnerability localization. Our key insight is that self-attention mechanisms assign varying importance to different parts of the input, making it possible to track how much attention the model focuses on specific lines of code. In the context of vulnerability localization, the hypothesis is that vulnerable lines of code will naturally attract higher attention weights because they have a greater influence on the model’s output. By systematically tracking changes in attention weights and focusing on specific lines of code, LOVA improves the precision of identifying vulnerable lines across various programming languages. Through rigorous experimentation and evaluation, we demonstrate that LOVA significantly outperforms existing LLM-based approaches, achieving up to a 5.3× improvement in F1-scores. LOVA also demonstrated strong scalability, with up to a 14.6× improvement in smart contract vulnerability localization across languages like C, Python, Java, and Solidity. Its robustness was proven through consistent performance across different LLM architectures. Copyright © 2024, The Authors. All rights reserved.

关键词： C (programming language)

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Understanding and Enhancing Issue Prioritization in GitHub

Understanding and Enhancing Issue Prioritization in GitHub

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IEEE International Conference on Automated software Engineering (ASE)

作者： Yingying He Wenhua Yang Minxue Pan Yasir Hussain Yu Zhou College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key Laboratory for Novel Software Technology Nanjing University China

GitHub has become a prominent platform for open source software development, facilitating collaboration and communication among a diverse group of contributors. Efficient issue tracking is a crucial aspect of managing projects on GitHub, and labels serve as one of the primary mechanisms for issue prioritization, while various other issue features are also utilized by issue handlers for the same purpose. However, in large projects, prioritizing issues remains a challenge, and the efficacy of using labels or other issue features for prioritization is not well understood. To address this knowledge gap, we conduct a comprehensive empirical study that investigates the role of labels in GitHub issue prioritization, examines the influence of various issue features on prioritization, and assesses the performance of different ranking algorithms based on these impactful features. Our study, conducted on a dataset comprising data from over 1.5 million issues across diverse GitHub projects, provides valuable insights for issue handling in open source platforms and offers guidance for future research in this domain. Specifically, the study reveals the limited effectiveness of labels in issue prioritization, highlights the significance of certain issue features in the prioritization process, and compares the performance of various ranking algorithms for issue prioritization to support issue handlers.

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SAPPX: Securing COTS Binaries with Automatic Program Partitioning for Intel SGX

SAPPX: Securing COTS Binaries with Automatic Program Partiti...

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International Symposium on software Reliability Engineering (ISSRE)

作者： Jiawei Huang Hao Han Fengyuan Xu Bing Chen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key Laboratory of Novel Software Technology Nanjing University China

In the era of cloud computing, many applications are migrated to public servers not fully controlled by users who may fear their critical operations or data from being compromised by attackers. Previous studies have shown that Intel SGX enclaves can improve applications’ security in many market products. Yet they mainly rely on developers to reprogram and recompile the application into an SGX-aware version. To address this problem, we propose SAPPX, an SGX-based program retrofitting method that can automatically partition COTS application binaries into two parts without breaking the original program semantics. The first part of the application runs in user space, while the second part is executed in an SGX enclave to protect the user’s sensitive information. We have implemented a prototype of SAPPX on x86/Linux platforms and evaluated its performance using real-world applications and SPECCPU 2017 benchmarks. The experimental results show that the average overhead of the proposed approach is up to 19%.

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An Innovative Approach To software Modularization Based On The Artificial Fish Swarm Algorithm 22

An Innovative Approach To Software Modularization Based On T...

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Proceedings of the 2022 6th International Conference on Electronic Information technology and computer Engineering

作者： Jianqiang Pan Cheng Zhang Huihui Jia Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology State Key Lab for Novel Software Technology Anhui University China

ISBN: (纸本)9781450397148

It gets more and more expensive to maintain the complete software system as time goes on since the software architecture grows more complicated and the software code is more difficult to understand. This issue may be solved by taking the essential parts out of the source code and organizing them into the appropriate subsystems. Hierarchy-based partitioning is more complex and less successful in solving issues with huge software modules. In order to do this, this work suggests an innovative artificial fish swarm method as a meta-heuristic to solve the software module clustering problems (SMCPs) and presents a mutation operator and a local search strategy to handle the premature situation. The performance of the suggested method is assessed on a range of real-world software systems by comparison to the most recent meta-heuristic methods. The outcomes of the experiments demonstrate that the suggested methodology works better than those of other methods.

关键词： software refactoring Artificial fish swarm algorithm software module clustering

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NBMon: NB-IoT-based UAV Network Routing Monitoring System 24

NBMon: NB-IoT-based UAV Network Routing Monitoring System

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Tang, Ji'ao Zhu, Xiaojun Dong, Chao Zhang, Lei College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics China Nanjing University State Key Laboratory of Novel Software Technology Nanjing China

ISBN: (纸本)9798350319934

The routing state of UAV networks is difficult to monitor, making many new routing protocols hard to evaluate on UAVs. In this paper, we propose NBMon, an NB-IoT-based routing monitoring system for UAV networks, which can upload the routing table information from the host device to a control station through the NB-IoT network. At the same time, the control station can also send commands to the monitored devices to change routes. However, one challenge is that both NB-IoT modules and the control station are behind NAT without global IP addresses. We propose a NAT traversal method on top of NB- IoT. It allows communication between the NB-IoT modules and the control station in different networks. Finally, we implement NBMon and evaluate the performance of NB-IoT devices through real-world experiments. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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