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11th International Conference on software and Computer Applications, ICSCA 2022

作者： Ding, Xiang Zhang, Cheng Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University China School of Computer Science and Technology Anhui University China State Key Lab for Novel Software Technology Nanjing University China

ISBN: (纸本)9781450385770

Context: software Architecture Smells (AS) are potential software structure problems and always impact software quality negatively. And with the development of Microservice architecture, the Microservice Architecture Smells (MAS) have been attracting more and more attention. The software scholars and developers have discovered the influence of MAS and performed some researches on them. However, the definition and category of MAS are still ambiguous. Objects: This paper aims to clarify the specific MAS categories and their definitions, and then further explores the issues caused by MAS in the migration process from a monolithic system to Microservice. Method: We performed a comprehensive systematic literature review about MAS. Specifically, we explored 13 white and 10 grey literature in detail to get MAS information by using the quantitative research method. To explore the issues that influence the migration process from a monolithic system to Microservice, we used the meta-ethnography qualitative research method to extract relevant information and get six three-order translations. Results: This study defined 22 Microservice Architecture Smells and classified them into five categories, namely Design, Deployment, Monitor & Log, communication and Team & Tool, based on their characteristics. Simultaneously, the issues that influence the migration process are proposed, including service cutting, databases, communication, team and techniques. Finally, we matched the MAS to the issues they caused in the migration process and recommended solutions to these issues. © 2022 ACM.

关键词： meta-ethnography Microservice Architecture Smells smell impact Systematic Literature Review

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Implementation of Domain-oriented Microservices Decomposition based on Node-attributed Network 2022

Implementation of Domain-oriented Microservices Decompositio...

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11th International Conference on software and Computer Applications, ICSCA 2022

作者： Cao, Lingli Zhang, Cheng Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University China School of Computer Science and Technology Anhui University China State Key Lab for Novel Software Technology Nanjing University China

ISBN: (纸本)9781450385770

The features of microservices, such as scalability and maintainability, have attracted the industry to migrate monolithic projects to microservices. However, how to decompose microservices during migration is a tricky problem. At present, microservices decomposition mainly relies on architects or domain experts, which is more subjective and time-consuming. Followed by semi-automated or automated microservice decomposition, such methods produce coarse-grained results affected by different system characteristics, which cannot make desired decomposition according to the migration requirements of domains. Therefore, this paper proposes a domain-oriented fine-grained microservices decomposition resolution scheme. It uses dynamic and static analysis to obtain the invocation relationships and invocation times between entity methods and the response time of entity methods to represent three main system characteristics concerned during the migration: function, inter-service communications, and performance. And express the above information of monolith by the node-attributed network. Then it uses the community detection algorithm and the proposed similar hierarchical clustering algorithm to complete objective and effective decomposition. Finally, the rationality and feasibility of the proposed approach are verified using the JPetStore case. © 2022 ACM.

关键词： Dynamic analysis Static analysis Microservices Decomposition Node-attributed Network Domain

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Multi-objective software test case selection based on density analysis 22

Multi-objective software test case selection based on densit...

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5th International Conference on Computer Science and software Engineering, CSSE 2022

作者： Jia, Huihui Zhang, Cheng Wu, Sijie Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University China School of Computer Science and Technology Anhui University China State Key Lab for Novel Software Technology Nanjing University Hefei China

ISBN: (纸本)9781450397780

software test case selection is committed to select the fewest test cases from test suites to perform a complete test at the least cost. Machine learning and multi-objective optimization techniques have developed rapidly in recent years, and they have been successfully applied to test case selection. In this paper, we present a method called DB-NSGA2, which uses the density clustering algorithm in machine learning combined with the non-dominated ranking algorithm (NSGA2) for test case selection, which can better select the test cases required for testing. In particular, we apply some of the clustering results generated by the clustering algorithm to the crossover and mutation operations of the NSGA2 to improve diversity progeny populations and ensure the transmission of good individuals. Extensive experiments show that the test cases selected by our method can produce a better set of Pareto solutions and can detect more faults at a lower cost than other methods. © 2022 ACM.

关键词： Clustering algorithms

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Chaos-based estimation method for software reliability

Chaos-based estimation method for software reliability

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2nd International Conference on Networks Security, Wireless communications and Trusted Computing, NSWCTC 2010

作者： Shao, Chenxi Huang, Chao MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei Anhui China Computer Science Department University of Science and Technology of China Hefei Anhui China Anhui Province Key Laboratory of Software in Computing and Communication Hefei Anhui China

ISBN: (纸本)9780769540115

Reliability is one of the essential attributes of the dependable software, and an important factor for quantitatively characterizing software quality. Conventional methodology is software Reliability Growth Model (SRGM), which specifies the form of a random process that describes the behavior of software failures with respect to time. In this paper, we propose that the behavior of software failures possesses determinacy as well as randomness. So we apply chaos theory to software reliability assessment. Based on chaos theory, we can estimate software reliability according to the objective law hidden in the data, unlike the SRGM which usually makes a number of assumptions. This approach avoids the subjectivity in the SRGM. Also, we analyze the actual software failures data sets with the add-weighted one-rank localregion method (AOLM) based on chaos theory and compare the results with the conventional stochastic SRGM JM (Jelinsky-Moranda) model and NHPP (nonhomogeneous poisson process) mode. Comparison results show that the proposed method fits better than the stochastic ones. © 2010 IEEE.

关键词： software reliability

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Cooperative spectrum sensing with feedback

Cooperative spectrum sensing with feedback

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5th International Conference on Wireless communications, Networking and Mobile Computing, WiCOM 2009

作者： Ping, Zhu Jinglong, Li Xufa, Wang Anhui Province Key Laboratory of Software in Computing and Communication School of Computer Science and Technology University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9781424436934

One of the main objective of spectrum sensing is to detect spectrum opportunity quickly and accurately. Both of the two aspects can be improved by hardware upgrading and software optimization. In this paper, we concentrate on sensing accuracy, which, differs from previous solutions, will be enhanced by modification of network topology. We expand the traditional parallel fusion network (PFN) to parallel feedback fusion network (PFFN), in which local cognitive sensor makes its local decision based on previous global decision and its recent observation. We then derived optimal decision rules for PFFN. Moreover, based on PFFN, sensing history of local cognitive sensor is introduced and studied. Theoretical analysis and experimental results show that PFFN outperforms the PFN in smaller system error probability under the same SNR. We also proved that PFFN and PFFN with sensing history (PFFNSH) emphasize on different aspects when certain condition holds in real world applications. Finally, a general PFFN framework is proposed and discussed. ©2009 IEEE.

关键词： Feedback

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Identification of Microservices through Processed Dynamic Traces and Static Calls 3

Identification of Microservices through Processed Dynamic Tr...

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3rd International Conference on Computer Science and Management Technology, ICCSMT 2022

作者： Wu, Sijie Zhang, Cheng Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University China Anhui University School of Computer Science and Technology China Nanjing University State Key Lab for Novel Software Technology Hefei China

ISBN: (纸本)9798350320404

As the complexity of software systems continues to increase, traditional monolithic software architectures will become difficult to scale and maintain. Microservices as an emerging software architecture it advocates decentralized management, service autonomy, and distinct module boundaries. These features of microservices make software systems easy to maintain, deploy, and modify. However, microservice identification is still not well addressed due to the lack of an effective approach. The existing work on microservice identification mainly starts from the system functionality perspective and does not take into account the actual business rules, which leads to microservice candidates not conforming to the concept of microservices. To address this challenge, we propose an automated approach to microservice identification that combines dynamic and static analysis and takes into account the actual business. Our proposed approach starts with a dynamic and static analysis of the monolithic system. Once the behavioral data of the system is collected, the behavioral data will be further processed according to our proposed guidelines. Then, the proposed approach calculates the degree of the correlation between classes in a monolithic system. Finally, we propose an improved clustering algorithm based on K-means to classify the classes in a monolithic system into different microservices. The proposed approach is validated by comparison with other approaches on open source projects. © 2022 IEEE.

关键词： K-means clustering

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WFSS: weighted fusion of spectral transformer and spatial self-attention for robust hyperspectral image classification against adversarial attacks

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Visual Intelligence 2024年第1期2卷 1-16页

作者： Tang, Lichun Yin, Zhaoxia Su, Hang Lyu, Wanli Luo, Bin Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Anhui China School of Communication & Electronic Engineering East China Normal University Shanghai China Department of Computer Science and Technology Tsinghua University Beijing China

The emergence of adversarial examples poses a significant challenge to hyperspectral image (HSI) classification, as they can attack deep neural network-based models. Recent adversarial defense research tends to establish global connections of spatial pixels to resist adversarial attacks. However, it cannot yield satisfactory results when only spatial pixel information is used. Starting from the premise that the spectral band is equally important for HSI classification, this paper explores the impact of spectral information on model robustness. We aim to discover potential relationships between different spectral bands and establish global connections to resist adversarial attacks. We design a spectral transformer based on the transformer structure to model long-distance dependency relationships among spectral bands. Additionally, we use a self-attention mechanism in the spatial domain to develop global relationships among spatial pixels. Based on the above framework, we further explore the influence of both spectral and spatial domains on the robustness of the model against adversarial attacks. Specifically, a weighted fusion of spectral transformer and spatial self-attention (WFSS) is designed to achieve the multi-scale fusion of spectral and spatial connections, which further improves the model’s robustness. Comprehensive experiments on three benchmarks show that the WFSS framework has superior defensive capabilities compared to state-of-the-art HSI classification methods. © The Author(s) 2024.

关键词： Adversarial attack Adversarial example Hyperspectral image (HSI) classification Self-attention Transformer

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Clustering time-evolving data using an efficient differential evolution 6th

Clustering time-evolving data using an efficient differentia...

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6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on computational Intelligence, CCI 2015

作者： Chen, Gang Luo, Wenjian Anhui Province key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China HefeiAnhui China

ISBN: (纸本)9783319204659

The previous evolutionary clustering methods for time-evolving data usually adopt the temporal smoothness framework, which controls the balance between temporal noise and true concept drift of clusters. They, however, have two major drawbacks: (1) assuming a fixed number of clusters over time;(2) the penalty term may reduce the accuracy of the clustering. In this paper, a Multimodal Evolutionary Clustering (MEC) based on Differential Evolution (DE) is presented to cope with these problems. With an existing chromosome representation of the ACDE, the MEC automatically determines the cluster number at each time step. Moreover, instead of adopting the temporal smoothness framework, we try to deal with the problem from view of the multimodal optimization. That is, the species-based DE (SDE) for multimodal optimization is adopted in the MEC. Thus the MEC is a hybrid of the ACDE and the SDE, and designed for time-evolving data clustering. Experimental evaluation demonstrates the MEC achieves good results. © Springer International Publishing Switzerland 2015.

关键词： Cluster analysis

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A clustering system for gene expression data based upon genetic programming and the HS-model

A clustering system for gene expression data based upon gene...

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3rd International Joint Conference on computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice

作者： Liu, Guiquan Jiang, Xiufang Wen, Lingyun Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9780769540306

Cluster analysis is a major method to study gene function and gene regulation information for there is a lack of prior knowledge for gene data. Many clustering methods existed at present usually need manual operations or predetermined parameters, which are difficult for gene data. Besides, gene data possess their own characteristics, such as large scale, high-dimension, and noise. Therefore, a systematic clustering algorithm should be proposed to effectively deal with gene data. In this paper, a novel genetic programming (GP) clustering system for gene data based on hierarchical statistical model (HS-model) is proposed. And an appropriate fitness function is also proposed in this system. This clustering system can largely eliminate the infection of data scale and dimension. The proposed GP clustering system is applied to cluster the whole intact yeast gene data without dimensionality reduction. The experimental results indicate that the algorithm is highly efficient and can effectively deal with missing values in gene dataset. © 2010 IEEE.

关键词： Cluster analysis

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A fast algorithm for finding community structure based on community closeness

A fast algorithm for finding community structure based on co...

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3rd International Joint Conference on computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice

作者： Jiang, Xiufang Liu, Guiquan Lin, Zhiting Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9780769540306

Recently, the characterization of community structures in complex networks has received a considerable amount of attentions. Effective identification of these communities or clusters is a general problem in the field of data mining. In this paper we present a fast hierarchical agglomerative algorithm based on community closeness (FHACC) algorithm, for detecting community structure which is very efficient and faster than many other competing algorithms. FHACC tends to agglomerate such communities that share the most common vertices into larger ones. Its running time on a sparse network with n vertices and m edges is O(mk¯+mt), where k¯ denotes the mean vertex degree, and t is the iteration times of community agglomeration in FHACC algorithm. The algorithm was tested on several real-world networks and proved to be high efficient and effective in community finding. © 2010 IEEE.

关键词： Complex networks

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