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SenDe, IDP, IEKB, MBC, International Workshops, APWeb 2012

作者： Yang, Zhuoluo You, Jinguo Zhou, Min School of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan China Yunnan Computer Technology Application Key Lab Kunming University of Science and Technology Kunming Yunnan China Software Technology Limited Company Hangzhou Zhejiang China

ISBN: (纸本)9783642294259

Due to current real-time data compression algorithms is not efficient enough, we have proposed a two-phase real-time data compression algorithm which can be very fast in data compression with high compression rate. The algorithm can adapt to both text and binary files. The first phase compresses the file with long common strings into short forms. The second phase compresses the result of the first phase with short bytes in common into the final results. We name the algorithm TDSC Algorithm. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Data compression

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Distinguishing LLM-generated from Human-written Code by Contrastive Learning

arXiv

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arXiv 2024年

作者： Xu, Xiaodan Ni, Chao Guo, Xinrong Liu, Shaoxuan Wang, Xiaoya Liu, Kui Yang, Xiaohu State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei Hangzhou China

Large language models (LLMs), such as ChatGPT released by OpenAI, have attracted significant attention from both industry and academia due to their demonstrated ability to generate high-quality content for various tasks. Despite the impressive capabilities of LLMs, there are growing concerns regarding their potential risks in various fields, such as news, education, and software engineering. Recently, several commercial and open-source LLM-generated content detectors have been proposed, which, however, are primarily designed for detecting natural language content without considering the specific characteristics of program code. This paper aims to fill this gap by proposing a novel ChatGPT-generated code detector, CodeGPTSensor, based on a contrastive learning framework and a semantic encoder built with UniXcoder. To assess the effectiveness of CodeGPTSensor on differentiating ChatGPT-generated code from human-written code, we first curate a large-scale Human and Machine comparison Corpus (HMCorp), which includes 550K pairs of human-written and ChatGPT-generated code (i.e., 288K Python code pairs and 222K Java code pairs). Based on the HMCorp dataset, our qualitative and quantitative analysis of the characteristics of ChatGPT-generated code reveals the challenge and opportunity of distinguishing ChatGPT-generated code from human-written code with their representative features. Our experimental results indicate that CodeGPTSensor can effectively identify ChatGPT-generated code, outperforming all selected baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Model Checking Quantum Continuous-Time Markov Chains 32

Model Checking Quantum Continuous-Time Markov Chains

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32nd International Conference on Concurrency Theory, CONCUR 2021

作者： Xu, Ming Mei, Jingyi Guan, Ji Yu, Nengkun MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9783959772037

Verifying quantum systems has attracted a lot of interests in the last decades. In this paper, we initialise the model checking of quantum continuous-time Markov chain (QCTMC). As a real-time system, we specify the temporal properties on QCTMC by signal temporal logic (STL). To effectively check the atomic propositions in STL, we develop a state-of-the-art real root isolation algorithm under Schanuel's conjecture;further, we check the general STL formula by interval operations with a bottom-up fashion, whose query complexity turns out to be linear in the size of the input formula by calling the real root isolation algorithm. A running example of an open quantum walk is provided to demonstrate our method. © Ming Xu, Jingyi Mei, Ji Guan, and Nengkun Yu;.

关键词： Model checking

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API Usage Recommendation via Multi-View Heterogeneous Graph Representation Learning

arXiv

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arXiv 2022年

作者： Chen, Yujia Ren, Xiaoxue Gao, Cuiyun Peng, Yun Xia, Xin Lyu, Michael R. Harbin Institute of Technology Shenzhen China The Chinese University of Hong Kong Hong Kong Hong Kong Software Engineering Application Technology Lab Huawei China

Developers often need to decide which APIs to use for the functions being implemented. With the ever-growing number of APIs and libraries, it becomes increasingly difficult for developers to find appropriate APIs, indicating the necessity of automatic API usage recommendation. Previous studies adopt statistical models or collaborative filtering methods to mine the implicit API usage patterns for recommendation. However, they rely on the occurrence frequencies of APIs for mining usage patterns, thus prone to fail for the low-frequency APIs. Besides, prior studies generally regard the API call interaction graph as homogeneous graph, ignoring the rich information (e.g., edge types) in the structure graph. In this work, we propose a novel method named MEGA for improving the recommendation accuracy especially for the low-frequency APIs. Specifically, besides call interaction graph, MEGA considers another two new heterogeneous graphs: global API co-occurrence graph enriched with the API frequency information and hierarchical structure graph enriched with the project component information. With the three multi-view heterogeneous graphs, MEGA can capture the API usage patterns more accurately. Experiments on three Java benchmark datasets demonstrate that MEGA significantly outperforms the baseline models by at least 19% with respect to the Success Rate@1 metric. Especially, for the low-frequency APIs, MEGA also increases the baselines by at least 55% regarding the Success Rate@1 score. Copyright © 2022, The Authors. All rights reserved.

关键词： application programming interfaces (API)

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Investigating White-Box Attacks for On-Device Models

Investigating White-Box Attacks for On-Device Models

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International Conference on software engineering (ICSE)

作者： Mingyi Zhou Xiang Gao Jing Wu Kui Liu Hailong Sun Li Li Monash University Melbourne VIC Australia Beihang University Beijing China Huawei Software Engineering Application Technology Lab China Beihang University Beijing Yunnan Key Laboratory of Software Engineering China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Numerous mobile apps have leveraged deep learning capabilities. However, on-device models are vulnerable to attacks as they can be easily extracted from their corresponding mobile apps. Although the structure and parameters information of these models can be accessed, existing on-device attacking approaches only generate black-box attacks (i.e., indirect white-box attacks), which are less effective and efficient than white-box strategies. This is because mobile deep learning (DL) frameworks like TensorFlow Lite (TFLite) do not support gradient computing (referred to as non-debuggable models), which is necessary for white-box attacking algorithms. Thus, we argue that existing findings may underestimate the harm-fulness of on-device attacks. To validate this, we systematically analyze the difficulties of transforming the on-device model to its debuggable version and propose a Reverse engineering framework for On-device Models (REOM), which automatically reverses the compiled on-device TFLite model to its debuggable version, enabling attackers to launch white-box attacks. Our empirical results show that our approach is effective in achieving automated transformation (i.e., 92.6%) among 244 TFLite models. Compared with previous attacks using surrogate models, REOM enables attackers to achieve higher attack success rates (10.23%→89.03%) with a hun-dred times smaller attack perturbations (1.0→0.01). Our findings emphasize the need for developers to carefully consider their model deployment strategies, and use white-box methods to evaluate the vulnerability of on-device models. Our artifacts 1 1 https://***/zhoumingyi/REOM are available.

关键词： Deep learning Analytical models Computational modeling Atmospheric modeling Reverse engineering Transforms Mobile applications

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Investigating White-Box Attacks for On-Device Models

arXiv

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arXiv 2024年

作者： Zhou, Mingyi Liu, Kui Gao, Xiang Sun, Hailong Wu, Jing Li, Li Monash University MelbourneVIC Australia Huawei Software Engineering Application Technology Lab China Beihang University Beijing China Beihang University Beijing Yunnan Key Laboratory of Software Engineering China

Numerous mobile apps have leveraged deep learning capabilities. However, on-device models are vulnerable to attacks as they can be easily extracted from their corresponding mobile apps. Although the structure and parameters information of these models can be accessed, existing on-device attacking approaches only generate black-box attacks (i.e., indirect white-box attacks), which are less effective and efficient than white-box strategies. This is because mobile deep learning (DL) frameworks like TensorFlow Lite (TFLite) do not support gradient computing (referred to as non-debuggable models), which is necessary for white-box attacking algorithms. Thus, we argue that existing findings may underestimate the harmfulness of on-device attacks. To validate this, we systematically analyze the difficulties of transforming the on-device model to its debuggable version and propose a Reverse engineering framework for On-device Models (REOM), which automatically reverses the compiled on-device TFLite model to its debuggable version, enabling attackers to launch white-box attacks. Our empirical results show that our approach is effective in achieving automated transformation (i.e., 92.6%) among 244 TFLite models. Compared with previous attacks using surrogate models, REOM enables attackers to achieve higher attack success rates (10.23%→89.03%) with a hundred times smaller attack perturbations (1.0→0.01). Our findings emphasize the need for developers to carefully consider their model deployment strategies, and use white-box methods to evaluate the vulnerability of on-device models. Our artifacts 1 are available. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Ratchet: Retrieval Augmented Transformer for Program Repair

Ratchet: Retrieval Augmented Transformer for Program Repair

引用

International Symposium on software Reliability engineering (ISSRE)

作者： Jian Wang Shangqing Liu Xiaofei Xie Jingkai Siow Kui Liu Yi Li Singapore Management University Singapore Nanyang Technological University Singapore Software Engineering Application Technology Laboratory Huawei China

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

Automated Program Repair (APR) presents the promising momentum of releasing developers from the burden of manual debugging tasks by automatically fixing bugs in various ways. Recent advances in deep learning inspire many works in employing deep learning techniques to fixing buggy programs. However, several challenges remain unaddressed: (1) state-of-the-art fault localization techniques often require additional artifacts, such as bug-triggering test cases or bug reports. These artifacts are not always available in the early development phases; (2) Sequence-to-Sequence model-based APR often requires additional contexts with high quality to generate patches. Yet, it is challenging to identify high-quality contexts that are not common in *** this paper, with the redundancy assumption in program repair, we propose a dual deep learning-based APR tool, RATCHET, for localizing (RATCHET-FL) and repairing (Ratchet-PG) buggy programs. Ratchet-FL localizes buggy statements based on the feature learned by a simple BiLSTM model from the code, without any bug-triggering test cases or bug reports. Ratchet-PG relies on our proposed retrieval augmented transformer to learn the historical patches and generate patches for fixing bugs. We evaluate the effectiveness of Ratchet with in-the-lab DrRepair dataset and in-the-wild dataset Ratchet-DS (curated in this work). Our experimental results show that Ratchet outperforms state-of-the-art deep learning approaches on fault localization with 39.8-96.4% accuracy and patch generation with 18.4-46.4% repair accuracy.

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Design of fine management system for civil aviation airspace resources based on spatiotemporal grid model 1

Design of fine management system for civil aviation airspace...

引用

1st IEEE International Conference on Civil Aviation Safety and Information technology, ICCASIT 2019

作者： Zheng, Yinger Liu, Juqing Li, Jun Xu, Yubin Huang, Qian Zhu, Yan Pei, Yanqiu Big Data Application Center China Academy of Civil Aviation Science and Technology Beijing China College of Geoscience and Surveying Engineering China University of Mining and Technology Beijing China Research and Development Center China Academy of Civil Aviation Science and Technology Beijing China Huawei Research Institute Huawei Technology Co. Ltd Service Lab Beijing China

ISBN: (纸本)9781728125985

Along with the rapid development of aviation industry, airspace demand is increasing. However, the current airspace resources are tight and the utilization efficiency is low. Aiming at addressing this problem, this paper proposes a fine management system for civil aviation airspace resources based on spatiotemporal grid model. Relying on massive, multisource, multi-scale and multi-dimensional aviation big data, this study expands the time dimension of global discrete grid technology and establishes a global unified spatiotemporal grid model. On this basis, the calculation and analysis method of the spatiotemporal grid model is designed to carry out route management, conduct flight collision detection, allocate airspace resources, and then realize efficient and intelligent management, allocation and intelligent application of airspace resources. Finally, the scientificity and feasibility of the management system is verified through the practical application of civil aviation ADS-B data, meteorological data and route data. © 2019 IEEE.

关键词： Civil aviation

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NEGAR: Network Embedding Guided Architecture Recovery for software Systems

NEGAR: Network Embedding Guided Architecture Recovery for So...

引用

Asia-Pacific Conference on software engineering

作者： Jiayi Chen Zhixing Wang Yuchen Jiang Jun Pang Tian Zhang Minxue Pan Jianwen Sun State Key Lab for Novel Software Technology Nanjing University Nanjing China Department of Computer Science University of Luxembourg Esch-sur-Alzette Luxembourg Huawei Technologies Software Engineering Application Technology Lab Shenzhen China

ISBN: (纸本)9781665455381

With their rapid development, the scale and complexity of software systems are rapidly growing. Identifying and organizing files of similar functionality into the same module, called architecture recovery, contributes to the maintainability of a software system. However, manual architecture recovery on large-sized software requires unbearable costs. Hence a lot of automatic algorithms have been proposed in recent years. However, current algorithms’ accuracy is still insufficient to support practical applications. To improve the accuracy of architecture recovery, this work proposes a novel algorithm, NEGAR, which leverages random walks to extract latent graphic information from the dependency graph of files in the software system and learn the node representation for clustering. The proposed algorithm NEGAR has been comprehensively evaluated on three medium-sized and two large-sized software systems, as well as a super large-sized software system, in terms of four widely-used metrics. The experimental results demonstrate the outstanding accuracy and excellent scalability of NEGAR.

关键词： Measurement Codes Scalability software algorithms Semantics Clustering algorithms Computer architecture

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Image super‐resolution via dynamic network

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CAAI Transactions on Intelligence technology 2024年第4期9卷 837-849页

作者： Chunwei Tian Xuanyu Zhang Qi Zhang Mingming Yang Zhaojie Ju School of Software Northwestern Polytechnical UniversityXi'anChina National Engineering Laboratory for Integrated Aero‐Space‐Ground‐Ocean Big Data Application Technology Xi'anChina Research&Development Institute Northwestern Polytechnical UniversityShenzhenChina School of Economics and Management Harbin Institute of Technology at WeihaiWeihaiChina Tencent AI Lab ShenzhenChina School of Computing University of PortsmouthPortsmouthUK

Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐***,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction *** residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐*** enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying *** prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained ***,a residual learning operation is embedded in the refinement block to prevent long‐term dependency ***,a construction block is responsible for reconstructing high‐quality *** heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital *** results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and *** code of DSRNet can be obtained at https://***/hellloxiaotian/DSRNet.

关键词： CNN dynamic network image super‐resolution lightweight network

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