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31st International Conference on software Engineering and Knowledge Engineering, SEKE 2019

作者： Yang, Xingguang Yu, Huiqun Fan, Guisheng Yang, Kang 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

ISBN: (纸本)1891706489

Just-in-time software defect prediction (JIT-SDP) is an active topic in the filed of software engineering, and many methods have been proposed to solve this problem. State-of-the-art method MULTI applies multi-objective optimization algorithm to the effort-aware JIT-SDP problem, and obtains good average performance. Although the average performance of the MULTI method is high, there are many optimal solutions with poor performance. If an optimal solution is randomly selected, a poor prediction model may be obtained. In order to further improve the performance of the MULTI method, we propose three optimal solutions selection strategies: benefit priority (BP), cost priority (CP), and a compromise between cost and benefit (CCB). In order to compare and validate the effectiveness of the strategies, we conduct a large-scale empirical study on data sets of six open source projects. The experimental results show that, compared with the average performance of MULTI, the optimal solutions selection strategy based on BP has a significant improvement in ACC and Popt indicators. Therefore, we recommend using the BP-based optimal solutions selection strategy to improve the performance of MULTI when using the MULTI method to solve the effort-aware JIT-SDP problem. © 2019 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Multiobjective optimization

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Neighborhood-based uncertain QoS prediction of web services via matrix factorization 14th

Neighborhood-based uncertain QoS prediction of web services ...

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

作者： Zou, Guobing Pang, Shengye Wang, Pengwei Miao, Huaikou Niu, Sen Gan, Yanglan Zhang, Bofeng School of Computer Engineering and Science Shanghai University Shanghai China School of Computer Science and Technology Donghua University Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China

ISBN: (纸本)9783030129804

With the rapidly overwhelming number of services on the internet, QoS-based web service recommendation has become an urgent demand on service-oriented applications. Since there are a large number of missing QoS values in the user historical invocation records, accurately predicting these missing QoS values becomes a hot research issue. However, most existing service QoS prediction research assumes that the transactional process of the service was stable, and its QoS doesn’t change as time goes. In fact, service invocation process is usually affected by many factors (e.g., geographical location, network environment), leading to service invocations with QoS uncertainty. Therefore, QoS prediction based on traditional methods can not exactly adapt to the scenarios in real-world applications. To solve the issue, combined with the collaborative filtering and matrix factorization theory, we propose a novel approach for prediction of uncertain service QoS under the dynamic Internet environment. Extensive experiments have been conducted on a real-world data set and the results demonstrate the effectiveness and applicability of our approach for QoS prediction. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Forecasting

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imBBO: An Improved Biogeography-Based Optimization Algorithm 13th

imBBO: An Improved Biogeography-Based Optimization Algorithm

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13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018

作者： Shi, Kai Yu, Huiqun Fan, Guisheng Yang, Xingguang Song, Zheng 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 The Third Research Institute of the Ministry of Public Security Shanghai China

ISBN: (纸本)9783030150921

Biogeography based Optimization (BBO) is a new evolutionary optimization algorithm based on the science of biogeography for global optimization. However, its direct-copying-based migration and random mutation operators make it easily possess local exploitation ability. To enhance the performance of BBO, we propose an improved BBO algorithm called imBBO. A hybrid migration operation is designed to further improve the population diversity and enhance the algorithm exploration ability. Empirical results demonstrate that our imBBO effectively gains the high optimization performance by comparing with the original BBO and three BBO variants for 23 out of 30 CEC’2017 benchmarks. Moreover, our imBBO presents a faster convergence speed. © 2019, Springer Nature Switzerland AG.

关键词： Global optimization

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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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