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Reinforcement Learning Application Testing Method based on Multi-attribute Fusion 9

Reinforcement Learning Application Testing Method based on M...

引用

9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Cai, Lizhi Wang, Jin Chen, Mingang Wang, Jilong School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Reinforcement learning has been successfully applied to assess the reliability of applications, but the existing testing methods based on reinforcement learning have the problems of invalid interactive widgets and difficult training, resulting in low testing efficiency. In order to solve these problems, this paper proposes a lightweight application automation testing method of deep reinforcement learning based on multi-attribute fusion (MARTesting). First, the invalid widgets are removed by the difference operation of the attribute sets of the current and previous state, then the attributes of all widget elements on the page are abstracted into state as the input of the neural network, and a state is accurately determined by fusing the position and text information of page elements, finally combine the novelty of the state and the execution frequency of the action as a reward function. The experimental results on six open source applications show that the MARTesting method proposed in this paper has achieved significant improvements in code coverage and branch coverage compared with existing methods. © 2022 IEEE.

关键词： Reinforcement learning

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AgileShard: Turning the Sharded Blockchain into a Real-Time Transaction Processing System 44

AgileShard: Turning the Sharded Blockchain into a Real-Time ...

引用

44th IEEE Real-Time Systems Symposium, RTSS 2023

作者： Shi, Jianfeng Wu, Heng Liu, Wang Gao, Heran Zhang, Wenbo University of Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Nanjing China Software Engineering Technology Research and Development Center Institute of Software Chinese Academy of Sciences Beijing China Nanjing Institute of Software Technology Nanjing China Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science Beijing China

ISBN: (纸本)9798350328578

Blockchain, as an emerging transaction processing system, suffers from low throughput and high latency. Sharded blockchains can significantly increase throughput by dividing nodes into groups (i.e., shards) to process disjoint transactions in parallel. However, the diverse latency requirements of transactions in current sharded blockchains are not well met. Three challenges prevent the sharded blockchain from becoming a real-time transaction processing system, namely, the static block size, the first-come-first-served transaction packing strategy, and the load imbalance. Therefore, this paper proposes 3 methods to help turn the sharded blockchain into a real-time transaction processing system. First, we propose an inter-shard dynamic block size negotiation method that enables shards to adaptively determine the globally optimal block size based on the deadlines of pending transactions. Then, we propose a DAG-based transaction packing method for reducing the number of deadline violations and improving parallelism. Finally, we propose a minimum-cost-flow-based shard reconfiguration method to address load imbalance. Under real datasets on Ethereum, experimental results show that AgileShard using the above three methods can effectively improve the transaction deadline satisfaction rate. © 2023 IEEE.

关键词： Blockchain

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Research on Quality Model for Quantum Simulator software 26

Research on Quality Model for Quantum Simulator Software

引用

26th ACIS International Winter Conference on software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Wang, Chao Chen, Jingwei Ge, JianXin Wu, XueZhong Qu, QianQian Luo, Le Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Qudoor Technologies Inc. Hefei China Software Engineering of Computer and Information Engineering College Shanghai Polytechnic University Shanghai China Qudoor Technologies Inc. School of Physics and Astronomy Sun Yat-Sen University Zhuhai China

ISBN: (纸本)9798350345865

In recent years, the demand for arithmetic power from all walks of life is growing, especially the increasing popularity of AIGC, which makes people realize that huge technological progress cannot be made without the support of large arithmetic power. Quantum computing may bring a leap in computing power and become a 'trigger' and 'catalyst' to accelerate the development of science and technology innovation, and its technical development trend and application prospects have become a hot issue of concern, and quantum simulators are a very important part of the development process. The development of quantum simulators is a very important part of the process. However, due to the lack of systematic evaluation theories and methods, it is difficult to quantify and analyze the quality of the increasing number of simulators on the market, which may hinder researchers from practicing their quantum algorithms objectively or developing quantum technologies. For these reasons, this paper proposes a quality model for quantum simulator software, which reflects the quality characteristics of simulators in seven aspects, such as functionality and performance efficiency, and selects some simulators for experimental validation of the proposed model to prove its rationality. © 2023 IEEE.

关键词： Quality control

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BTM: Black-Box Testing for DNN Based on Meta-Learning 23

BTM: Black-Box Testing for DNN Based on Meta-Learning

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23rd IEEE International Conference on software Quality, Reliability, and Security, QRS 2023

作者： Zhang, Zhuangyu Zhang, Zhiyi Wang, Ziyuan Chen, Fang Huang, Zhiqiu Nanjing University of Aeronautics and Astronautics Collage of Computer Science and Technology Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing China Nanjing University of Aeronautics and Astronautics Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing China

ISBN: (纸本)9798350319583

Deep learning is widely used in security fields like autonomous driving, but testing deep learning models poses challenges due to low generation efficiency and limited error detection. Current white-box test case generation methods rely on neuron coverage, but black-box testing is crucial when model details cannot be accessed. Prior methods also needed more consideration for error diversity under lower time resources. In this paper, we propose a novel approach based on the intuition that two models trained on the same classification task learn similar rules at a coarse-grained level. We employ meta-learning to train universal meta perturbation on a surrogate model, which increases neuron coverage on the surrogate model while inducing misclassifications in the original model (thus enhancing the adequacy of the initial tests). Subsequently, we apply data initialization to discover a wider range of errors faster. To accelerate test case generation and reduce resource consumption, we introduce a set of acceleration techniques based on image prediction probabilities, minimizing the time spent exploring irrelevant regions in images. Finally, we evaluate our approach on a well-known dataset and several well-known models, demonstrating its effectiveness. © 2023 IEEE.

关键词： Black-box testing

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Analysis for Microservice Architecture Application Quality Model and Testing Method

Analysis for Microservice Architecture Application Quality M...

引用

software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter), ACIS International Winter Conference on

作者： Jiayu Gong Lizhi Cai Shanghai Key Laboratory of Computer Software Testing & Evaluation Shanghai Development Center of Computer Software Technology Shanghai China

In recent years, microservice architectures have benefited from low coupling and high cohesion characteristics, allowing flexible on-demand deployment of complex applications and simplifying the difficulty of developing and maintaining large software systems. However, there is limited research on quality models and testing methods for microservice architectures, which leads to the problem that microservice architecture applications are difficult to test and evaluate. This paper analyzes the main problems when microservice architecture applications face quality measurement and software testing, further improves and refines the quality model of microservice architecture applications on the basis of the existing ISO software product quality model, and introduces testing methods for microservice architecture applications, which can provide reference for the construction of microservice architecture applications.

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Research on Measuring Method of Function Size of Information System

Research on Measuring Method of Function Size of Information...

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software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter), ACIS International Winter Conference on

作者： Du Chunye Shanghai Key Laboratory of Computer Software Testing &Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

With the rapid development of software industry, a variety of new technologies are produced. Research and development in cloud computing, big data, artificial intelligence, the Internet of Things, blockchain, and other related fields continue to prosper. The increasing lines of code, function points, escalating scale of software systems, and the rising complexity of software development collectively complicate software cost estimation. This article studies how to estimate the size, workload, and cost of an information system using functional scale measurement methods.

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Method of adaptive learning feature model fusion and verification based on metamodels 3

Method of adaptive learning feature model fusion and verific...

引用

3rd International Symposium on computer Applications and Information Systems, ISCAIS 2024

作者： Wang, Chenyu Zhu, Yi Wu, Zixin Hao, Guosheng School of Computer Science and Technology Jiangsu Normal University Xuzhou221000 China Jiangsu Province Education Informatization Engineering Technology Research Center Jiangsu Normal University Xuzhou221000 China The Key Laboratory of the Software Development and Verification Technology The Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9781510681316

As the advancement of computer technique and artificial intelligence technology, education based on computer technology has become a hot topic, and the wave of educational informatization is rising. Among them, adaptive learning has received widespread attention as a type of educational informatization technology. However, the current adaptive learning feature models use different semantics to describe each other, and the models lack meta model level explanations, resulting in a lack of further operability of the models and difficulty in integrating with each other. In order to solve these problems, our paper proposes a fusion and verification method for learning feature adaptive models based on meta models. Above all, an adaptive learning feature meta model is proposed, which defines the construction rules of the adaptive learning feature model at meta model level, so that different models can be fused using the same set of semantic rules. Then, at the meta model level, transform the learning model into a requirement model. Finally, using formal methods, validate the fused feature model, which can make sure of the semantic consistency between the previous and subsequent models. This article demonstrates the entire process through a model fusion experiment. The experimental results indicate that this method is correct and feasible in promoting feature model fusion without adaptive learning. © 2024 SPIE.

关键词： Informatization

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Research on Recognition Method of Floating Objects on Water Surface Based on improved ResNet 6

Research on Recognition Method of Floating Objects on Water ...

引用

6th International Conference on Electronic Information technology and computer Engineering, EITCE 2022

作者： Guo, Haonan Ma, Yangyi Hua, Lei Wan, D. College of Computer and Information Hohai University China Software Development Center Bank of Communications China School of Management Tianjin University of Technology China

ISBN: (纸本)9781450397148

With the continuous population growth and rapid economic development, problems such as scarcity and pollution of freshwater resources are increasingly emerging. Floating objects on the water surface are one of the causes of water pollution, so the research on the identification algorithm of floating objects is of great significance. To further improve the accuracy of the identification of floating objects on the water surface, this paper proposes a DS-ResNet model based on the improved ResNet. Compared to previous work, DS-ResNet employs several innovations to improve training and testing speed while also increasing recognition accuracy. We established an actual data sample set in the field of rivers and lakes, and a modified ResNet model was constructed by Tensorflow for training. The experimental results show that the training cycle time of DS-ResNet is reduced by about 7% compared with the traditional ResNet, and the recognition accuracy of common targets reaches 98%. © 2022 Association for Computing Machinery.

关键词： Deep learning

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RSChat:Intelligent Question Answering Model for Railway Safety Knowledge

RSChat:Intelligent Question Answering Model for Railway Safe...

引用

International Conference on computer and Information Science (ACIS)

作者： Jinyu Li Chao Li Sen Niu Bingrong Dai School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (数字)9798350376647

ISBN: (纸本)9798350376654

The release of ChatGPT heralds a new era in artificial intelligence, as large language models (LLMs) empower myriad industries. This paper introduces RSChat, a model based on supervised fine-tuning of multiple large language models (LLMs), exploring a novel paradigm for applying knowledge in railway safety. Initially, we perform prompt engineering using collected domain data, including key information extraction and knowledge expansion. Subsequently, leveraging the constructed fine-tuning datasets, we conduct supervised fine-tuning using multiple large language models (LLMs) as base models. Finally, experimental evaluations demonstrate that RSChat can generate more professional responses in the railway safety domain, exhibiting robust potential for application.

关键词： Knowledge engineering Industries Information science Large language models Computational modeling Railway safety Information retrieval Chatbots Question answering (information retrieval) Prompt engineering

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CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning 37

CLGT: A Graph Transformer for Student Performance Prediction...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Peng, Tianhao Liang, Yu Wu, Wenjun Ren, Jian Pengrui, Zhao Pu, Yanjun State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology China Institute of Artificial Intelligence Beihang University China

ISBN: (纸本)9781577358800

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Students

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