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software project management Approaches for Global software Development： A Systematic Mapping Study

Tsinghua Science and Technology 2018年第6期23卷 690-714页

作者： Manal El Bajta Ali Idri Joaquín Nicolás Ros José Luis Fernández-Alemán Juan Manuel Carrillo de Gea Félix García Ambrosio Toval software project management research team Ecole Nationale SuperieuredInformatique et d'Analyse des Systemes (ENSIAS)Mohammed V University Rabat 713 Morocco Department of Informaticay Sistemas University of Murcia Murcia 30100 Spain ALARCOS research GroupDepartment of Information Systems and Technologies University of Castilla-La Mancha (UCLM) Ciudad Real13071 Spain

Global software Development （GSD） is a well established field of software engineering with the benefits of a global environment. software project management （SPM） plays a key role in the success of GSD. As a result, the need has arisen to study and evaluate the downsides of SPM for GSD, to thereby pave the way for the development of new methods, techniques, and tools with which to tackle them. This paper aims to identify and classify research on SPM approaches for GSD that are available in the literature, to identify their current weaknesses and strengths, and to analyze their applications in industry. We performed a Systematic Mapping Study （SMS） based on six classification criteria. Eighty-four papers were selected and analyzed. The results indicate that interest in SPM for GSD has been increasing since 2006. As a class of approaches, the most frequently reported methods （40%） are those used for coordination, planning, and monitoring, along with estimation techniques that can be used to better match a distributed project. SPM for GSD requires further investigation by researchers and practitioners, particularly with respect to cost and time estimations. These findings will help overcome the challenges that must to be considered in future SPM research for GSD, especially regarding collaboration and time-zone differences.

关键词： software project management (SPM) Global software Development (GSD) SPM approaches Systematic Mapping Study (SMS)

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Machine Learning Techniques for software Maintainability Prediction:Accuracy Analysis

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Journal of Computer Science & Technology 2020年第5期35卷 1147-1174页

作者： Sara Elmidaoui Laila Cheikhi Ali Idri Alain Abran Softtrare project management TeamEcole Nationale Superieure d'Informattique et d'Analyse des SystemesMadinate Al IrfaneMohammed V University in Rabat.Agdal RabatBP 713Morocco Department of software Engineering&Infonnation Technology Ecole de Technologie Superieure(ETS)Montreal HSC IKSCanada

Maintaining software once implemented on the end-user side is laborious and,over its lifetime,is most often considerably more expensive than the initial software *** prediction of software maintainability lias emerged as an important research topic to address industry expectations for reducing costs,in particular,maintenance *** and practitioners have been working on proposing and identifying a variety of techniques ranging from statistical to machine learning(ML)for better prediction of software *** review has been carried out to analyze the empirical evidence on the accuracy of software product maintainability prediction(SPMP)using ML *** paper analyzes and discusses the findings of 77 selected studies published from 2000 to 2018 according to the following criteria:maintainability prediction techniques,validation methods,accuracy criteria,overall accuracy of ML techniques,and the techniques offering the best *** review process followed the well-known syslematic review *** results show that ML techniques are frequently used in predicting *** particular,artificial neural network(ANN),support vector machine/regression(SVM/R).regression&decision trees(DT),and fuzzy neuro fuzzy(FNF)techniques are more accurate in terms of PRED and *** N-fold and leave-one-out cross-validation methods,and the MMRE and PRED accuracy criteria are frequently used in empirical *** general,ML techniques outperformed non-machine learning techniques,e.g.,regression analysis(RA)techniques,while FNF outperformed SVM/*** ANN in most ***,while many techniques were reported superior,no specific one can be identified as the best.

关键词： accuracy criterion accuracy value machine learning technique maintainability prediction

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Chinese Journal of Electronics 2021年第1期30卷 55-63页

作者： BO Lili ZHU Xuanrui SUN Xiaobing NI Zhen LI Bin School of Information Engineering Yangzhou University Jiangsu Engineering research Center of Knowledge management and Intelligent Service State Key Laboratory for Novel software Technology Nanjing University

Fine-grained change operations can help software developers fix software bugs more accurately and efficiently. However, the current fine-grained change operations are only used in specific fixing process, such as fixing of If statement. In this paper, we conducted an empirical study to explore the fine-grained change operations for bug fixing. Based on the Mozilla bug data,we examined whether similar bugs are fixed with similar change operations. The results show that: First, for bug reports with similar descriptions or bug-fix commits with similar descriptions, their corresponding fine-grained change operations are not related; Second, in the case where the descriptions of both bug reports and bug-fix commits are similar, the fine-grained change operations in patch code are not related; Third, by classifying bug reports, we find that the change operations in the same bug report category are similar; Finally, by analyzing the fine-grained change operations for each bug, we present some combined patterns that are often used together.

关键词： bug reports bug fixing If statement Mozilla bug data bug-fix commits similar descriptions program debugging fine-grained change operations

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Enhanced Constructability Using 4D Plus Modelling in Brownfield Sites

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Journal of Building Construction and Planning research 2023年第2期11卷 36-49页

作者： Michael A. Stogdill Muhammed Asharaf T. Veleri project management team—OFPDNGPD Saudi Aramco Dhahran Kingdom of Saudi Arabia

In the oil and gas industry, operations in greenfield sites are straightforward. However, there are increased risks, such as damage to existing facilities, unknown site conditions, and other complexities in brownfield sites. In addition to these physical risks, task scheduling is also complex, as vessels need to be replaced during a turnaround. To mitigate the risks of site conditions and realize a truncated schedule for Saudi Aramco’s operations, 3D laser scanning was combined with 3D modeling, clash detection, and time. This integrated approach minimized the shutdown period, which resulted in cost savings, heightened safety, and enhanced stakeholder communications.

关键词： Brownfield Laser Scanning 4-D Modeling 3-D Modeling Constructability

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Towards a self healing information system for digital libraries

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2005年第11期6卷 1221-1228页

作者： AMBATI Vamshi REDDY Raj Institute for software research International Carnegie Mellon University Pittsburgh PA 15213 USAInstitute for Software Research International Carnegie Mellon University Pittsburgh PA 15213 USA

An important area of focus in complex systems development is the capability to adapt to variable runtime environmental resources and to accommodate runtime system failures. The research in this area is broadly termed as “Self Healing” and has recently aroused increasing attention to complex systems. Digital libraries have gained popularity because of the richer features they provide compared to traditional libraries. As more users begin to use digital libraries, addressing downtimes of these valuable resources has become a high priority. Operating and providing access to digital content to anyone, anytime from anywhere in the world, results in a continued rise of administrative overhead for system monitoring and needs continuous human intervention. Given the volume of information and the huge infrastructure of modem libraries, continuous manual system administration is quite costly and not feasible. In this paper we propose a self healing digital library system as the solution to this problem and present the approach of adding self healing capabilities to an existing digital library project, the Digital Library of India （DLI）. We also propose a self healing framework that enables successful reuse of our approach to other architecturally similar digital library systems.

关键词： Digital library Self healing systems Failover Configuration management

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Detecting overlapping communities in networks via dominant label propagation

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Chinese Physics B 2015年第1期24卷 551-559页

作者：孙鹤立黄健斌田勇强宋擒豹刘怀亮 Department of Computer Science and Technology Xi’an Jiaotong University State Key Laboratory for Novel software Technology Nanjing University School of software Xidian University School of Economics and management Xidian University

Community detection is an important methodology for understanding the intrinsic structure and function of a realworld network. In this paper, we propose an effective and efficient algorithm, called Dominant Label Propagation Algorithm（Abbreviated as DLPA）, to detect communities in complex networks. The algorithm simulates a special voting process to detect overlapping and non-overlapping community structure in complex networks simultaneously. Our algorithm is very efficient, since its computational complexity is almost linear to the number of edges in the network. Experimental results on both real-world and synthetic networks show that our algorithm also possesses high accuracies on detecting community structure in networks.

关键词： overlapping community detection dominant label propagation complex network

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Data and knowledge-driven named entity recognition for cyber security

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Cybersecurity 2021年第1期4卷 123-135页

作者： Chen Gao Xuan Zhang Hui Liu School of software Yunnan University650091 YunnanChina Key Laboratory of software Engineering of Yunnan Province 650091 YunnanChina Engineering research center of cyberspace 650091 YunnanChina

Named Entity Recognition(NER)for cyber security aims to identify and classify cyber security terms from a large number of heterogeneous multisource cyber security *** the field of machine learning,deep neural networks automatically learn text features from a large number of datasets,but this data-driven method usually lacks the ability to deal with rare *** et *** a deep learning method for named entity recognition in the field of cyber security,and achieved good results,reaching an F1 value of 82.8%.But it is difficult to accurately identify rare entities and complex words in the *** cope with this challenge,this paper proposes a new model that combines data-driven deep learning methods with knowledge-driven dictionary methods to build dictionary features to assist in rare entity *** addition,based on the data-driven deep learning model,an attentionmechanism is adopted to enrich the local features of the text,better models the context,and improves the recognition effect of complex *** results show that our method is better than the baseline *** model is more effective in identifying cyber security *** Precision,Recall and F1 value reached 90.19%,86.60%and 88.36%respectively.

关键词： Cyber security Named entity recognition Attention mechanism Dictionary Deep learning

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Developer Role Evolution in Open Source software Ecosystem： An Explanatory Study on GNOME

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Journal of Computer Science & Technology 2017年第2期32卷 396-414页

作者： Can Cheng Bing Li Zeng-Yang Li Yu-Qi Zhao Feng-Ling Liao International School of software Wuhan University Wuhan 430072 China State Key Laboratory of software Engineering Wuhan University Wuhan 430072 China Complex Networks research Center Wuhan University Wuhan 430072 China

An open source software （OSS） ecosystem refers to an OSS development community composed of many software projects and developers contributing to these projects. The projects and developers co-evolve in an ecosystem. To keep healthy evolution of such OSS ecosystems, there is a need of attracting and retaining developers, particularly project leaders and core developers who have major impact on the project and the whole team. Therefore, it is important to figure out the factors that influence developers＇ chance to evolve into project leaders and core developers. To identify such factors, we conducted a case study on the GNOME ecosystem. First, we collected indicators reflecting developers＇ subjective willingness to contribute to the project and the project environment that they stay in. Second, we calculated such indicators based on the GNOME dataset. Then, we fitted logistic regression models by taking as independent variables the resulting indicators after eliminating the most collinear ones, and taking as a dependent variable the future developer role （the core developer or project leader）. The results showed that part of such indicators （e.g., the total number of projects that a developer joined） of subjective willingness and project environment significantly influenced the developers＇ chance to evolve into core developers and project leaders. With different validation methods, our obtained model performs well on predicting developmental core developers, resulting in stable prediction performance （0.770, F-value）.

关键词： developer role evolution open source software ecosystem project environment subjective willingness

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Linking changes in the soil microbial community to C and N dynamics during crop residue decomposition

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Journal of Integrative Agriculture 2021年第11期20卷 3039-3059页

作者： Cyrine REZGUI Isabelle TRINSOUTROT-GATTIN Marie BENOIT Karine LAVAL Wassila RIAH-ANGLET AGHYLE research Unit UniLaSalleRouen-TeamMont-Saint Aignan 76134France Agroecology and Environment research Unit IsaraAgroSchool for LifeLyon 69007France

Crop residues are among the main inputs that allow the organic carbon(C)and nutrients to be maintained in agricultural *** is an important management strategy that can improve soil fertility and enhance agricultural *** work aims to evaluate the extent of the changes that may occur in the soil heterotrophic microbial communities involved in organic matter decomposition and C and nitrogen(N)mineralization after the addition of crop *** microcosm experiments were performed at 28℃ for 90 days with the addition of three crop residues with contrasting biochemical qualities:pea(Pisum sativum L.),rapeseed(Brassica napus L.),and wheat(Triticum aestivum L.).Enzyme activities,C and N mineralization,and bacterial and fungal biomasses were monitored,along with the bacterial and fungal community composition,by the highthroughput sequencing of 16 S rRNA and ITS *** addition of crop residues caused decreases in β-glucosidase and arylamidase activities and simultaneous enhancement of the C mineralization and net N immobilization,which were linked to changes in the soil microbial *** addition of crop residues decreased the bacterial and fungal biomasses 90 days after treatment and there were shifts in bacterial and fungal diversity at the phyla,order,and genera *** specific orders and genera were dependent on crop residue *** example,Chloroflexales,Inquilinus,Rubricoccus,Clitocybe,and Verticillium were identified in soils with pea residues;whereas Thermoanaerobacterales,Thermacetogenum,and Hypoxylon were enriched in soils with rapeseed residues,and Halanaerobiales,Rubrobacter,and Volutella were only present in soils with wheat *** findings of this study suggest that soil C and N dynamics in the presence of the crop residues were driven by the selection of specific bacterial and fungal decomposers linked to the biochemical qualities of the crop *** crop residue decomposition processes showed specific bacteria

关键词： crop residues C and N mineralization enzyme activities bacterial and fungal diversity

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A novel virtual machine deployment algorithm with energy efficiency in cloud computing

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Journal of Central South University 2015年第3期22卷 974-983页

作者：周舟胡志刚宋铁于俊洋 School of software Central South University

In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the energy consumption and(processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is,host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies(minimization of migrations policy based on TESA(MIMT), maximization of migrations policy based on TESA(MAMT), highest potential growth policy based on TESA(HPGT), lowest potential growth policy based on TESA(LPGT) and random choice policy based on TESA(RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold(ST) algorithm and minimization of migrations(MM) algorithm, MIMT significantly improves the energy efficiency in data centers.

关键词： cloud computing energy efficiency three-threshold virtual machine(VM) selection policy energy management

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