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检索条件"机构=Shanghai Key Laboratory of Computer Software Evaluating and Testing"

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Template-based Formalization of Feature Models for Product Family 13

Template-based Formalization of Feature Models for Product F...

引用

13th IEEE International Conference on software Engineering and Service Science, ICSESS 2022

作者： Liu, Hongbo Wang, Xi Wang, Weiwei Shanghai University School of Computer Engineering and Science Shanghai China Shanghai University Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai200444 China

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

The feature model considers several products as a collection of features and describes the commonality and variability of the family in terms of feature relationships. Traditional informal feature models lacked the support of tools to automate the process of feature modeling, so formal methods were introduced implement automated support for feature modeling tools. For large software, writing formal specifications for feature models directly is quite difficult and requires technical background and experience. In this paper, we propose a formal template-based approach to constructing feature models and their formal descriptions, each template is used to formalize a class of PFA expressions. We give a structural definition of the templates and for constructing feature models from templates and generating formal descriptions of the corresponding features. We give a case study to illustrate the effectiveness of our method. © 2022 IEEE.

关键词： Writing Reliability engineering software software reliability Formal specifications testing software engineering

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Efficiency-First Fault-Tolerant Replica Scheduling Strategy for Reliability Constrained Cloud Application 18th

Efficiency-First Fault-Tolerant Replica Scheduling Strategy ...

引用

18th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2021

作者： Zhang, Yingxue Fan, Guisheng Yu, Huiqun Chen, Xingpeng Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030935702

Reliability requirement assurance is an important prerequisite for application execution in the cloud. Although copy management can improve the reliability of applications, it also brings a series of resource waste and overhead issues. Therefore, the efficiency-first fault-tolerant algorithm (EFFT) with minimum execution cost in the cloud application is proposed. This algorithm minimizes the execution cost of the application under the constraints of reliability, and solves the problem of excessive overhead caused by too many copies. The EFFT algorithm is divided into two stages: initial allocation and dynamic adjustment. On the initial allocation of EFFT algorithm, a sorting rule is defined to determine the priority of tasks and instances. During the adjustment phase, by defining an actual efficiency ratio indicator to measure the cost-effectiveness of an instance, the EFFT algorithm makes a good trade-off between cost and reliability in order to minimize execution costs. Run our algorithm on randomly generated parallel applications of different scales and compare the experimental results with four advanced algorithms. The experiments show that the performance of the algorithm we proposed is better than the other algorithms in terms of execution cost and fault tolerance. © 2022, IFIP International Federation for Information Processing.

关键词： Fault tolerance

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SFPL: Improving CRS via Prompt Learning based Semantic Fusion Module

SFPL: Improving CRS via Prompt Learning based Semantic Fusio...

引用

Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Ao Song Zhou Lei Shengbo Chen College of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

With the popularity of the Internet and the massive increase of content production, users are faced with massive information and content, and it is often difficult to accurately and efficiently find the content that meets their needs. This information overload motivates the emergence of recommendation systems that help users filter and recommend relevant content. Dialogue recommendation systems (CRS) aim to dynamically recommend high-quality items to users while eliciting user preferences by means of dialogue. CRS mainly consists of two parts, namely, the dialogue module and the recommendation module. An efficient CRS is to solve the problems existing in the two sub-tasks of dialogue and recommendation, namely, generating appropriate responses in dialogue and predicting user favorite items in recommendation. In addition, it is necessary to adopt a suitable method to integrate these two modules to ensure the accuracy of CRS recommendation. This paper proposes an efficient way of semantic fusion module to solve the integration problem of two modules, and proves the effectiveness of our method through experiments

关键词： Semantics Training data Production Learning (artificial intelligence) Knowledge representation Information filters Data models Internet Information technology Recommender systems

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ALQ: An Efficient Active Learning Recommendation Algorithm

ALQ: An Efficient Active Learning Recommendation Algorithm

引用

IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

With the rapid development of the Internet, the problem of information overload is becoming more and more serious, which makes it difficult for users to choose the content they are interested in from a large number of information. With the emergence of recommendation systems, the historical interaction information between users and information and items is used to provide users with topics of interest, which not only alleviates the problem of information overload, but also brings great satisfaction to users. However, training a model that can make accurate recommendations is not an easy task. In addition to an appropriate training strategy, it also requires a large amount of data support. Getting data, especially labeled data, takes a lot of time and money. Therefore, this paper proposes a new active learning algorithm. Firstly, the recommendation problem was transformed into a PU learning problem, and then the method was introduced in the positive sample learning process to achieve the purpose of increasing positive sample data, thereby reducing the cost of data labeling. Through experiments, the proposed method reduces the cost of data labeling in the recommendation task and further verifies the rationality of transforming the recommendation problem into a PU learning problem.

关键词： Training Costs Transforms Classification algorithms Internet Information management Labeling

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Rethinking Generalizable Face Anti-Spoofing via Hierarchical Prototype-Guided Distribution Refinement in Hyperbolic Space

Rethinking Generalizable Face Anti-Spoofing via Hierarchical...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Chengyang Hu Ke-Yue Zhang Taiping Yao Shouhong Ding Lizhuang Ma Shanghai Jiao Tong University Youtu Lab Tencent Shanghai Key Laboratory of Computer Software Evaluating and Testing

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Generalizable face anti-spoofing (FAS) approaches have drawn growing attention due to their robustness for diverse presentation attacks in unseen scenarios. Most previous methods always utilize domain generalization (DG) frame-works via directly aligning diverse source samples into a common feature space. However, these methods neglect the hierarchical relations in FAS samples which may hinder the generalization ability by direct alignment. To address these issues, we propose a novel Hierarchical Prototype-guided Distribution Refinement (HPDR)framework to learn embedding in hyperbolic space, which facilitates the hierarchical relation construction. We also collaborate with prototype learning for hierarchical distribution refinement in hyperbolic space. In detail, we propose the hierarchical Prototype Learning to simultaneously guide domain alignment and improve the discriminative ability via constraining the multi-level relations between prototypes and instances in hyperbolic space. Moreover, we design a Prototype-oriented Classifier, which further considers relations between the sample and prototypes to improve the robustness of the final decision. Extensive experiments and visualizations demonstrate the effectiveness of our method against previous competitors.

关键词： Visualization computer vision Face recognition Prototypes Robustness Faces

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A framework for Requirements specification of machine-learning systems 34

A framework for Requirements specification of machine-learni...

引用

34th International Conference on software Engineering and Knowledge Engineering, SEKE 2022

作者： Wang, Xi Miao, Weikai School of Computer Engineering and Science Shanghai University China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201114 China Software Engineering Institute East China Normal University China

ISBN: (纸本)1891706543

The rapid development of machine learning (ML) systems has raised many concerns over their quality. Due to the inherent complexity and uncertainty, most of the traditional quality assurance techniques have been challenged, including requirements specification. Current strategies mainly focus on model extraction from existing neural networks to improve interpretability and facilitate system analysis, but failing to include user expectations on the system. To handle the problem, this paper proposes a specification framework for ML requirements where each ML system is regarded as a set of snapshot systems along the evolvement process. There are 3 layers in the framework and the hierarchy indicates that higher-level models need to be built based on lower-level ones. The bottom layer consists of meta snapshot model and meta data model serving as the meta models for snapshot systems and data requirements respectively. The middle layer is for snapshot models each describing a snapshot system through relations between its outputs produced with different inputs. The top layer is a learning model capturing the evolvement process by transitions among snapshot models. These transitions are activated by data models instantiated from meta data model. We adopt the specification of a self-driving system to illustrate the framework. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Metadata

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Research on Quality Model for Quantum Simulator software 26

Research on Quality Model for Quantum Simulator Software

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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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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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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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PMMVar: Leveraging Multi-level Protein Structures for Enhanced Coding Variant Pathogenicity Prediction

PMMVar: Leveraging Multi-level Protein Structures for Enhanc...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Yijia Chen, Yiwen Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China National University of Singapore Center for Continuing and Lifelong Education Singapore The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

Genomic variants, which can disrupt cellular functions, present a challenge in distinguishing deleterious from benign instances. While assessing genome-wide functional impacts, many current algorithms neglect protein tertiary structure of coding region variants due to limitations in protein structural prediction. This study introduces PMMVar, an advanced multimodal deep convolutional network, which adeptly integrates protein tertiary structures with conservation properties from ESM-2, supplemented by other protein structural sequences. PMMVar achieves outstanding performance on the latest clinical variant datasets, NCBI ClinVar (2023), and the Mendelian variant dataset, surpassing existing benchmarks. Ablation analyses validate the significance of protein multi-level structures in enhancing the model's accuracy. Overall, our findings spotlight the essential role of multi-level protein structures in pathogenicity predictions and their potential to discern deleterious genomic variants effectively. © 2023 IEEE.

关键词： deep learning multimodal pathogenicity protein structure variant

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