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The cuckoo search and integer linear programming based approach to time-aware test case prioritization considering execution environment 14th

The cuckoo search and integer linear programming based appro...

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14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Wong, Yu Zeng, Hongwei Miao, Huaikou Gao, Honghao Yang, Xiaoxian School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai200444 China Computing Center Shanghai University Shanghai200444 China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China

ISBN: (纸本)9783030129804

Regression testing plays an important role in software development process. The more mature software system development is, the greater the proportion of regression testing during software life cycle takes. To this point, test case prioritization techniques are proposed to detect more faults as early as possible and improve the effectiveness of regression testing. However, it is often performed in a time constrained execution environment. This paper introduces a new method of time-aware test case prioritization. First of all, it takes advantage of the cuckoo search algorithm to reorder test suite. Then, integer linear programming model is employed to test selection in light of time budget. At last, a novel fitness function is designed focusing on code coverage that from method-call information perspective. Experimental results show that our method improves the effectiveness of fault detection compared with traditional fault detection techniques especially time is constrained. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Fault detection

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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...

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

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

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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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A Dynamic Planning Framework for QoS-Based Mobile Service Composition Under Cloud-Edge Hybrid Environments 15th

A Dynamic Planning Framework for QoS-Based Mobile Service Co...

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

作者： Gao, Honghao Huang, Wanqiu Zou, Qiming Yang, Xiaoxian School of Computer Engineering and Science Shanghai University Shanghai China Computing Center Shanghai University Shanghai China Shanghai Shang Da Hai Run Information System Co. Ltd. Shanghai China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030301453

In cloud-edge hybrid environments, when QoS constraints of the SOA-based mobile service composition change, a dynamic reconfiguration needs to be performed. Different from the traditional cloud service, the cloud-edge hybrid environment has the characteristics of limited resource storage, limited energy at the edge and uncertain users who move frequently. Dynamic reconfiguration in this mode is challenging. QoS is an important indicator of service evaluation. Most studies focus on only the static QoS attributes of the service. However, the QoS of a service is not statically constant;it changes dynamically over time. Therefore, to avoid the immediate failure of the service and ensure the stability of the mobile service composition after dynamic reconfiguration, an LSTM neural network is applied to predict the future QoS value for candidate service. This value is used as a service evaluation indicator during dynamic reconfiguration. Then, attributes such as energy consumption, traffic and moving track are considered. A cost-reward mechanism is constructed to calculate the cost and reward of the service when it is invoked. The reasonable restriction conditions are added for controlling dynamic reconfiguration. Finally, the dynamic reconfiguration problem-solving process and framework for mobile service composition based on QoS in a cloud-edge hybrid environment is introduced, guiding the mobile service composition dynamic reconfiguration task in cloud-edge hybrid environments. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Quality of service

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Comparison of query languages for semantic database models 2018

Comparison of query languages for semantic database models

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2018 International Conference on Computing and Data Engineering, ICCDE 2018

作者： Thiam, Ablaye Xu, Dong Miao, Huaikou Niu, Zhihua School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Key Laboratory of Computer Software Evaluating and Testing China

ISBN: (纸本)9781450363938

A basic model of data illustrates the logical structure of a database including the relations and the constraints which determine how the data is stored and its accessibility. Individual database models are based upon the rules and concepts of a broader data model adopted by designer. Most data models can be represented with basic tables and diagrams. This paper will address the data model semantic and their interaction with Query languages. It will also discuss the general methods for implementing data models and data concepts, as they are applied to a wide range of semantic data models. © 2018 Association for Computing Machinery.

关键词： Semantics

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Test Case Generation for Web Application Based on Markov Reward Process

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Journal of Physics: Conference Series 2021年第1期1792卷

作者： Jing Cao Xiaoqiang Liu Hui Guo Lizhi Cai Yun Hu College of Computer Science and Technology Donghua University Shanghai 201620 China Shanghai Key Laboratory of Computer Software Testing &Evaluation Shanghai 201112 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.

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A Test Data Design Method for Data Quality Evaluation

A Test Data Design Method for Data Quality Evaluation

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The 2018 2nd International Conference on Advances in Energy, Environment and Chemical Science (AEECS 2018)

作者： Zhenyu LIU Qiang CHEN Lizhi CAI Shanghai Key Laboratory of Computer Software Testing and Evaluating

The rapid development of data analyze needs high data quality requirements. The data quality also determine the software quality. This paper proposes a test data deisgn method for evaluating data quality. The paper analyzes the data quality requirement model of typical software applications, considering the typical data quilty problem or issus. According to different evaluation characteristics, the paper proposes a test data method for quality evalaution related to data requirement. The paper also gives detailed for data design to fulfill the evlauation requirements. The method is used to test some data set and found some data issues in actual application databases.

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Segmentation of lesions in skin image based on salient object detection with deeply supervised learning 4

Segmentation of lesions in skin image based on salient objec...

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4th IEEE International Conference on computer and Communications, ICCC 2018

作者： Ji, Weijia Cai, Lizhi Chen, Wei Chen, Mingang Chai, Gang Laboratory of Computer Software Testing Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Department of Plastic and Reconstructive Surgery Shanghai Ninth People's Hospital Shanghai JiaoTong University School of Medicine Shanghai China

ISBN: (纸本)9781538683392

In this paper, a binary segmentation method HCDS based on salient object detection is proposed to solve the segmentation of lesions in skin images. The original U-Net model encounters some problems in its application to skin image segmentation, one of which is the non-pathological region information remained in the segmentation results. Our HCDS method improves its basic structure by adding a hybrid convolution module to the direct connection between the down-sampling and up-sampling. It deepens the abstract understanding of the input features in shallow layers and further eliminate the information interference of the non-pathological regions. Besides, the method employs a deeply supervised structure in each stage of up-sampling to learn from the output feature and ground truth. Finally, the HCDS integrates the multi-path outputs to obtain the best result. The experimental results show that the proposed method HCDS can effectively segment the lesion regions from the skin images. Our evaluation metrics about the HCDS are obviously superior to the original U-Net's. © 2018 IEEE.

关键词： Image segmentation

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A novel hybrid collaborative filtering approach to recommendation using reviews:the product attributes perspective 30

A novel hybrid collaborative filtering approach to recommend...

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30th International Conference on software Engineering and Knowledge Engineering, SEKE 2018

作者： Cao, Min Zhou, Sijing Gao, Honghao Li, Youhuizi School of Computer Engineering and Science Shanghai University Shanghai200444 China Computing Center Shanghai University Shanghai200444 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China College of Computer Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)1891706446

The product recommendation research has been focusing on modelling users' reviews to construct the relation of users and products. Thus, the recommended performance can be improved by obtaining virtual ratings from corresponding reviews. However, these perspectives on reviews do not take into account the product field characteristic, which may impact the recommendation performance. To this point, this paper proposes a hybrid collaborative filtering approach to compute the correlation value considering product attributes. First, Product Attribute Weight and Product Attribute Score are introduced to formalize the product attributes for user and product respectively in a quantitative way. After that, the recommended ranking formula for the new model is presented. Finally, we carry out experimental analysis to show our method can effectively improve the performance of recommendation under a sparseness dataset. © 2018 Universitat zu Koln. All rights reserved.

关键词： Collaborative filtering

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Stock technical analysis system based on real-time stream processing

Stock technical analysis system based on real-time stream pr...

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作者： Ding, Wencheng Wang, Hongya Peng, Nan Xiao, Yingyuan Liu, Zhenyu School of Computer Science and Technology Donghua University Shanghai China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Shanghai Key Laboratory of Computer Software Testing and Evaluation Shanghai China

ISBN: (纸本)9781538636749

Nowadays a large number of applications requires continuous and timely processing of information as it flows from the periphery to the center of the system. Among numerous tools that support these scenarios, the Esper engine is one of the most excellent and practical ones. In this paper, we utilize the Esper engine and a series of financial models to design and implement Stock Technical Analysis System Based on Real-time Stream Processing(STAS). Several general financial models, such as the capital asset pricing model, Candlestick chart, KDJ index and MACD index, are introduced to fulfill the requirements from different users. All these models or indexes are supported by the Esper engine which has a superiority in real-time processing. In this system, users can choose their favorite stocks based on annualized income and visualize these selected stocks in the form of data list, graph and analysis report. © 2017 IEEE.

关键词： Esper Engine Real-time Stream Processing Stock Technical Analysis

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