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

共 329 条记录，以下是151-160 订阅

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Multi-label Recommendation of Web Services with the Combination of Deep Neural Networks 15th

Multi-label Recommendation of Web Services with the Combinat...

引用

15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Gan, Yanglan Xiang, Yang Zou, Guobing Miao, Huaikou Zhang, Bofeng School of Computer Science and Technology Donghua University Shanghai201620 China School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030301453

With the increasing number of web services on the Internet, how to effectively classify and recommend service labels has become a research issue. It plays an important role in web service organization and management. However, the deficiency of current approaches is that they either recommend only a single label for a web service or a set of independent labels without order ranking that is still difficult for service providers to publish their web services. In this paper, together with label embedding techniques, we propose a novel approach for service multi-label recommendation using deep neural networks. Unlike traditional approaches, the predicted service labels of our approach not only satisfy the demands of service multi-label recommendation, but also provide the importance with an ordered label ranking. The experiments are conducted to validate the effectiveness on a large-scale dataset from ProgrammableWeb, involving 13,869 real-world Web services. The experimental results demonstrate that our approach for multi-label recommendation of web services outperforms the competing approaches in terms of multiple evaluation metrics. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Deep neural networks

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A study for extended regular expression-based testing 16

A study for extended regular expression-based testing

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16th IEEE/ACIS International Conference on computer and Information Science, ICIS 2017

作者： Liu, Pan Ai, Jun Xu, Zhenning Jimmy College of Information and Computer Shanghai Business School Shanghai201400 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China School of Reliability and System Engineering Beihang University Beijing China School of Business University of Southern Maine United States

ISBN: (纸本)9781509055074

software testing has become an essential activity to guarantee software quality. To reduce the overall cost of software testing, model-based testing has been widely studied in the past two decades and Finite State Machine (FSM) is used to build the model of software behaviors. However, due to the inadequacy of the modeling ability of FSM, FSM-based testing cannot be taken as a test oracle to solve all issues in software testing. To improve the modeling capability of the model, a few researchers have proposed using Extended Regular Expressions (ERE) to model software behaviors. This paper reviews the method of the ERE-based testing and presents six modeling rules to convert program codes to the ERE model. Then, a case is adopted to illustrate the process of generating executable paths from the ERE model and the method of designing test cases by those paths. Compared with the traditional graphic traversal method of constructing executable paths from the program, ERE not only has robust modeling capability to describe more types of software behaviors than FSM, but also can be used to construct effective executable paths to detect program errors. © 2017 IEEE.

关键词： software testing

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Automated Functionality and Security Evaluation of Large Language Models

Automated Functionality and Security Evaluation of Large Lan...

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IEEE International Conference on Smart Cloud (SmartCloud)

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

ISBN: (数字)9798350389500

ISBN: (纸本)9798350389517

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.

关键词： Cloud computing Toxicology Accuracy Graphics processing units Chatbots Security Task analysis

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An empirical study on progressive sampling for just-in-time software defect prediction 7

An empirical study on progressive sampling for just-in-time ...

引用

7th International Workshop on Quantitative Approaches to software Quality, QuASoQ 2019

作者： Yang, Xingguang Yu, Huiqun Fan, Guisheng Yang, Kang Shi, Kai Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Shanghai Engineering Research Center of Smart Energy Shanghai China Alibaba Group Hangzhou China

Just-in-time software defect prediction (JIT-SDP) is an active research topic in the field of software engineering, aiming at identifying defect-inducing code changes. Most existing JIT-SDP work focuses on improving the prediction performance of the model by improving the model. However, a frequently ignored problem is that collecting large and high quality defect data sets is costly. Specifically, when labelling the samples, experts in the field are required to carefully analyze the defect report information and log of code modification, which requires a lot of effort. Therefore, how to build a high-performance JIT-SDP model with a small number of training samples is an issue worth studying, which can reduce the size of the defect data sets and reduce the cost of data sets acquisition. This work thus provides a first investigation of the problem by introducing a progressive sampling method. Progressive sampling is a sampling strategy that determines the minimum number of training samples while guaranteeing the performance of the model. However, progressive sampling requires that the learning curve of the prediction model be well behaved. Thus, we validate the availability of progressive sampling in the JIT-SDP issue based on six open-source projects with 227417 changes. Experimental results demonstrate that the learning curve of the prediction model is well behaved. Therefore, the progressive sampling is feasible to tackle the JITSDP problem. Further, we investigate the optimal training sample size derived by progressive sampling for six projects. Empirical results demonstrate that a high-performance prediction model can be built using only a small number of training samples. Thus, we recommend adopting progressive sampling to determine the size of training samples for the JIT-SDP problem. Copyright © 2019 for this paper by its authors.

关键词： Defects

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

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

引用

Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

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

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

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.

关键词： Seminars Industries Optical character recognition Passwords Fuzzing Manipulators Information leakage

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Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identification 38

Learning Commonality, Divergence and Variety for Unsupervise...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shi, Jiangming Yin, Xiangbo Zhang, Yachao Zhang, Zhizhong Xie, Yuan Qu, Yanyun Institute of Artificial Intelligence Xiamen University China School of Informatics Xiamen University China Shanghai Innovation Institute China East China Normal University China Shanghai Key Laboratory of Computer Software Evaluating and Testing China

Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match specified persons in infrared images to visible images without annotations, and vice versa. USVI-ReID is a challenging yet underexplored task. Most existing methods address the USVI-ReID through cluster-based contrastive learning, which simply employs the cluster center to represent an individual. However, the cluster center primarily focuses on commonality, overlooking divergence and variety. To address the problem, we propose a Progressive Contrastive Learning with Hard and Dynamic Prototypes for USVI-ReID. In brief, we generate the hard prototype by selecting the sample with the maximum distance from the cluster center. We reveal that the inclusion of the hard prototype in contrastive loss helps to emphasize divergence. Additionally, instead of rigidly aligning query images to a specific prototype, we generate the dynamic prototype by randomly picking samples within a cluster. The dynamic prototype is used to encourage variety. Finally, we introduce a progressive learning strategy to gradually shift the model's attention towards divergence and variety, avoiding cluster deterioration. Extensive experiments conducted on the publicly available SYSU-MM01 and RegDB datasets validate the effectiveness of the proposed method. © 2024 Neural information processing systems foundation. All rights reserved.

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Test case generation for web application based on markov reward process

Test case generation for web application based on markov rew...

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2020 International Conference on Communications, Information System and software Engineering, CISSE 2020

作者： Cao, Jing Liu, Xiaoqiang Guo, Hui Cai, Lizhi Hu, Yun College of Computer Science and Technology Donghua University Shanghai201620 China Shanghai Key Laboratory of Computer Software Testing &Evaluation Shanghai201112 China

Web applications often face continuous updating due to functional change or UI renew, while it remains a challenge to guarantee their correctness. The goal of software testing is to find defects in a limited time range whereas exhaustive testing is an ideal yet time-consuming process. In this research, we propose an approach to generating test cases automatically based on the Markov reward process which innovatively contains a reward function for test results to guide the generation of test cases. By using the N-step algorithm, this approach can generate the test flow with the highest risk priority which can capture software defects as quickly as possible. The experiment on an e-commerce system shows that there is significant improvement on the defect detection capability of test cases generated through Markov reward process. © 2021 Journal of Physics: Conference Series.

关键词： software testing

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CIXG: A Comprehensive Approach to Driver Gene Identification and Causal Interpretation

CIXG: A Comprehensive Approach to Driver Gene Identification...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Yawen 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 University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350386226

With the ongoing advancements in science and technology and the increasing research focus on cancer-related issues, there has been a proliferation of omics-related resources for in-depth analysis and exploration. This burgeoning volume and complexity of biological data have fostered the integration of machine-learning techniques into biology. As a result, numerous machine-learning strategies have been established to identify driver mutations. Yet, many of these strategies produce complex models, complicating comprehension and thereby clouding the impact of input features on the resulting predictions. Our analysis presented the CIXG framework, which integrates a driver gene prediction module using XGBoost with a causality interpretation module anchored on CXPlain. This architecture enables quantifying each input feature's contribution to the prediction outcome and ensures precise predictions of driver genes. When benchmarked against the state-of-the-art (SOTA) method, CIXG demonstrated superior accuracy in pinpointing driver genes across pan-cancer studies and within the 32 specific cancer types. Importantly, our results underscored that mutation features chiefly influence CIXG's predictive prowess, with additional support from other omics features. © 2024 IEEE.

关键词： Adversarial machine learning

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Test case reusability metrics model

Test case reusability metrics model

引用

2010 2nd International Conference on computer Technology and Development, ICCTD 2010

作者： Zhang, Juan Cai, Lizhi Tong, Weiqing Yuan, Song Li, Ying School of Computer Engineering and Science Shanghai University Shanghai China Jiangxi University of Finance and Economics Jiangxi China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China East China University of Science and Technology Shanghai China Nanjing XiaoZhuang University NanJing China

ISBN: (纸本)9781424488438

As organizations implement systematic reuse test cases in software testing to improve productivity and quality, they must be able to measure reusability of test case and identify the most effective reuse strategies. This is done with Test Case Reusability Metrics Models(TCRMM). In this article we survey metrics of test case reusability, and provide reusability factors division that will help establish reusability assessment model. It combines four metrics factors into reusability metrics based on analytic hierarchy process. The prominent characteristic of this model is that the process of reusability metric is automated when the judgment matrix has been constructed. © 2010 IEEE.

关键词： Reusability

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Preliminaries of Bigraphical Calculus for Context-awareness

Preliminaries of Bigraphical Calculus for Context-awareness

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2017 2nd International Conference on Computational Modeling, Simulation and Applied Mathematics（CMSAM 2017)

作者： Dong XU Guang-yuan ZHOU Xiao-jun Li School of Computer Engineering and Science Shanghai University Shanghai Key Laboratory of Computer Software Evaluating & Testing

Context-awareness has become one of the active topics in ubiquitous computing, cloud computing and so on, recently. In this paper, preliminaries of bigraph based context-aware calculus are presented for context-aware system. We define the syntax and semantics of process, context and context expressions, as well as the semantics of operations in the bigraphical calculus of context-awareness.

关键词： Bigraph Context-awareness Bigraphical calculus

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