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11th International Conference on Mobile Computing, Applications, and Services, MobiCASE 2020

作者： Hu, Rui Chen, Mingang Cai, Lizhi Chen, Wenjie Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9783030642136

Recently, mobile devices are more popular than computers. However, mobile apps are not as thoroughly tested as desktop ones, especially for graphical user interface (GUI). In this paper, we study the detection and segmentation of graphical elements on GUIs for mobile apps based on deep learning. It is the preliminary work of GUI testing for mobile apps based on artificial intelligence. We create a dataset, which consists of 2,100 GUI screenshots (or pages) labeled with 42,156 graphic elements in 8 classes. Based on our dataset, we adopt Mask R-CNN to train the detection and segmentation of graphic elements on GUI screenshots. The experimental results show that the mAP value achieves 98%. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Graphical user interfaces

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software quality testing model for mobile application

Software quality testing model for mobile application

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11th International Conference on Mobile Web Information Systems, MobiWIS 2014

作者： Liu, Zhenyu Hu, Yun Cai, Lizhi Shanghai Key Laboratory of Computer Software Testing and Evaluation Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9783319103587

With the rapid development of the network technology, intelligent device and mobile applications has been the developed fastly. The mobile device will increasingly widely used even replace the traditional computer, the application test for mobile Internet was put on the agenda. From this paper, the characteristics of mobile applications are analyzed. The paper proposed quality model and quality attributes corresponding to testing requirements for mobile applications under mobile Internet. Also relevant properties testing techniques and methods is given to pay attention during the test from different test view, which indicating that the quality of the final model could be effective for mobile applications. © 2014 Springer International Publishing.

关键词： computer software selection and evaluation

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Automated Functionality and Security Evaluation of Large Language Models 9

Automated Functionality and Security Evaluation of Large Lan...

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9th IEEE International Conference on Smart Cloud, SmartCloud 2024

作者： Ding, Minjie Shen, Ying Chen, Mingang Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9798350389500

Natural language processing (NLP) is rapidly developing. A series of Large Language Models (LLMs) have emerged, represented by ChatGPT, which have made significant breakthroughs in natural language understanding and generation, enabling fluent dialogue with humans, understanding human intentions, and completing complex tasks. However, in addition to the fairness and toxicity of traditional language models, some new problems, including hallucination, have also emerged in LLMs, making them hard to use. Evaluating LLMs manually is challenging due to subjectivity and inefficiency. In this paper, we focused on the fuzzy matching, toxicity detection, and hallucination detection in the evaluation of LLMs automatically, and fine-tune the Mixtral-8x7B Model, which can be deployed in private cloud environment, and prove the effectiveness of our method through experiments. © 2024 IEEE.

关键词： Toxicity

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Enhanced Practical Byzantine Fault Tolerance for Service Function Chain Deployment: Advancing Big Data Intelligence in Control Systems

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computers, Materials and Continua 2025年第3期83卷 4393-4409页

作者： Peiying Zhang Yihong Yu Jing Liu Chong Lv Lizhuang Tan Yulin Zhang Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China Library of Shanghai Lixin University of Accounting and Finance Shanghai 201209 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan 250014 China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing 100081 China Key Laboratory of Intelligent Game Yangtze River Delta Research Institute of NPU Taicang 215400 China Key Laboratory of Education Informatization for Nationalities (Yunnan Normal University) Ministry of Education Kunming 650092 China

As Internet of Things (IoT) technologies continue to evolve at an unprecedented pace, intelligent big data control and information systems have become critical enablers for organizational digital transformation, facilitating data-driven decision making, fostering innovation ecosystems, and maintaining operational stability. In this study, we propose an advanced deployment algorithm for Service Function Chaining (SFC) that leverages an enhanced Practical Byzantine Fault Tolerance (PBFT) mechanism. The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings. By integrating blockchain technology and Deep Reinforcement Learning (DRL), our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment. Specifically, the enhanced PBFT consensus mechanism (VRPBFT) significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function (VRF) and a node reputation grading model. Experimental results demonstrate that compared to traditional PBFT, the proposed VRPBFT algorithm reduces consensus latency by approximately 30% and decreases the proportion of Byzantine nodes by 40% after 100 rounds of consensus. Furthermore, the DRL-based SFC deployment algorithm (SDRL) exhibits rapid convergence during training, with improvements in long-term average revenue, request acceptance rate, and revenue/cost ratio of 17%, 14.49%, and 20.35%, respectively, over existing algorithms. Additionally, the CPU resource utilization of the SDRL algorithm reaches up to 42%, which is 27.96% higher than other algorithms. These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency, service quality, and security in SFC deployment.

关键词： Big data intelligent transformation heterogeneous networks service function chain blockchain deep reinforcement learning trusted deployment

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STCG: State-Aware Test Case Generation for Simulink Models 23

STCG: State-Aware Test Case Generation for Simulink Models

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Zhuo Su Zehong Yu Dongyan Wang Yixiao Yang Rui Wang Wanli Chang Aiguo Cui Yu Jiang KLISS BNRist School of Software Tsinghua University Beijing China Information Technology Center Renmin University of China Beijing China Information Engineering College Capital Normal University Beijing China Department of Computer Science University of York York United Kingdom HUAWEI Technologies Co. LTD. Shanghai China

ISBN: (纸本)9798350323481

Simulink has been widely used in system design, which supports the efficient modeling and synthesis of embedded controllers, with automatic test case generation to simulate and validate the correctness of the constructed Simulink model. However, the increasing complexity of the model, especially the internal states, brings extra challenges to existing model testing techniques such as constraint solving and random search, which results in difficulties when trying to reach the deeper logic of the model *** this paper, we propose STCG, a state-aware test case generation method for Simulink models. STCG solves only one iteration of the model each time to get the test input that can cover a target branch, then executes the model once to obtain and update the novel model state based on the solved input dynamically. Then, it solves the remaining branches based on the new model state iteratively until all the coverage requirements are satisfied. We implemented STCG and evaluated it on several benchmark Simulink models. Compared to the built-in Simulink Design Verifier and state-of-the-art academic work SimCoTest, STCG achieves an average improvement of 58% and 132% on Decision Coverage, 52% and 70% on Condition Coverage and 239% and 237% on Modified Condition Decision Coverage, respectively.

关键词： test case generation

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IVIFUZZER: A Fuzzing method for vulnerability exploitation on the operating interface of the In-Vehicle Infotainment system 5

IVIFUZZER: A Fuzzing method for vulnerability exploitation o...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Wu, Jianhua Zhang, Xin Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China

ISBN: (纸本)9798350385557

As an important information system of intelligent and connected vehicles, most In-Vehicle Infotainment systems are set up and manage the vehicles through user interaction. This paper presents a Fuzzing method IVIFUZZER for vulnerability exploitation on the operation interface of the In-Vehicle Infotainment system. With the help of automated robotic arm and improved OCR technology, collecting system leakage information and directional detection of the identified input points, 6 zero day vulnerabilities were found. The experimental results show that the proposed IVIFUZZER method is effective. © 2024 IEEE.

关键词： Vehicles

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Survey on Testing of Autonomous Driving Systems 3

Survey on Testing of Autonomous Driving Systems

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3rd International Workshop on Experience with SQuaRE Series and Its Future Direction, IWESQ 2021

作者： Chen, Wenjie Ma, Zeyu Wang, Chao Chen, Mingang Cai, Lizhi Software Engineering Institute Shanghai Key Laboratory of Computer Software Testing & Evaluation Shanghai Development Center of Computer Software Technology Shanghai China

Before autonomous driving vehicles are commercialized, they need to undergo a series of rigorous tests. This paper first proposes the general process of autonomous driving system testing, and then summarizes the research progress of autonomous driving vehicle testing from the model, simulation, network, and vehicle levels, and analyzes the characteristics of various testing technologies. Finally, this paper gives suggestions on the development of autonomous driving testing technology. © 2021 for this paper by its authors

关键词： Autonomous vehicles

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Research on Quality Model and Measurement for Microservices 3

Research on Quality Model and Measurement for Microservices

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3rd International Workshop on Experience with SQuaRE Series and Its Future Direction, IWESQ 2021

作者： Yu, Jinchuan Ge, Jianxin Sun, Jixin Software Engineering Institute Shanghai Key Laboratory of Computer Software Testing & Evaluation Shanghai Development Center of Computer Software Technology Shanghai China

In recent years, microservice architecture (MSA) has become popular. Emerging from the agile community, MSA implies a number of small, independently deployable microservices. They are characterized by low coupling, high cohesion, low complexity, and are more flexible and convenient in application, saving resource ***, there is limited research on quality models for MSA. Although MSA is a special form of service-oriented architecture, there are still some differences between the two that are hard to ignore, such as decentralization, smaller service size and encouraging technical heterogeneity. Therefore, it is difficult to directly apply the traditional quality model research of service-oriented architecture to MSA. Based on the above considerations, this paper proposes a quality model for MSA, which reflects the quality characteristics of microservices through 16 measures for each of the Functional Suitability, Flexibility, Interactivity, Performance Efficiency, Reliability and Security. At the same time, to address the shortage of theoretical validation of current research, this paper uses static analysis and dynamic analysis to validate the proposed measures and prove the rationality of its theory. © 2021 for this paper by its authors

关键词： Static analysis

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Hybrid Intelligent Approach for the Selection of Third-Party Reverse Logistics Provider under Uncertainty

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Journal of Donghua University(English Edition) 2014年第4期31卷 484-492页

作者：宫艳雪宋俊典彭亦功添玉郑树泉 Intelligent Product Innovation Center Shanghai Industrial Technology Institute Shanghai Development Center of Computer Software Technology School of Information East China University of Science and Technology College of Logistics Engineering Shanghai Maritime University

A hybrid intelligent approach is proposed to help the decision maker to select the appropriate third-party reverse logistics provider. The following process is included: firstly,the evaluation team is established to determine the selection criteria and evaluate them by triangular fuzzy numbers; secondly,calculate the weight of criteria by the proposed hybrid algorithm integrating particle swarm optimization( PSO) and simulated annealing( SA); then, the performance evaluation for each supplier is predicted by the proposed self-feedback neural network( SFBNN) based on the historical data. A numerical example is also presented to interpret the methodology above.

关键词： hybrid intelligent approach third-party reverse logistics provider uncertainty

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H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic

arXiv

引用

arXiv 2025年

作者： Luo, Xueting Deng, Hao Yang, Jihong Shen, Yao Guo, Huanhuan Sun, Zhiyuan Liu, Mingqing Wei, Jiming Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai201804 China China Land Surveying and Planning Institute Beijing100035 China College of Architecture and Urban Planning Tongji University Shanghai200092 China Chongqing Institute of Planning and Natural Resources Investigation and Monitoring Chongqing401121 China Li-Fi Research and Development Centre University of Cambridge CambridgeCB3 0FA United Kingdom Guangdong Urban-rural Planning and Design Research Institute Technology Group Co. Ltd. Guangzhou510290 China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai201804 China

The necessity of achieving an effective balance between minimizing the losses associated with restricting human mobility and ensuring hospital capacity has gained significant attention in the aftermath of COVID-19. Reinforcement learning (RL)-based strategies for human mobility management have recently advanced in addressing the dynamic evolution of cities and epidemics;however, they still face challenges in achieving coordinated control at the township level and adapting to cities of varying scales. To address the above issues, we propose a multi-agent RL approach that achieves Pareto optimality in managing hospital capacity and human mobility (H2-MARL), applicable across cities of different scales. We first develop a township-level infection model with online-updatable parameters to simulate disease transmission and construct a city-wide dynamic spatiotemporal epidemic simulator. On this basis, H2-MARL is designed to treat each division as an agent, with a trade-off dual-objective reward function formulated and an experience replay buffer enriched with expert knowledge built. To evaluate the effectiveness of the model, we construct a township-level human mobility dataset containing over one billion records from four representative cities of varying scales. Extensive experiments demonstrate that H2-MARL has the optimal dual-objective trade-off capability, which can minimize hospital capacity strain while minimizing human mobility restriction loss. Meanwhile, the applicability of the proposed model to epidemic control in cities of varying scales is verified, which showcases its feasibility and versatility in practical applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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