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Mobile application testing in industrial contexts: An exploratory multiple case-study 1

14th International Conference on New Trends in Intelligent software Methodology, Tools, and Techniques, SoMeT 2015

作者： Zein, Samer Salleh, Norsaremah Grundy, John Department of Computer Science International Islamic University Kuala Lumpur Malaysia Centre for Computing and Engineering Software Systems Swinburne University of Technology Melbourne Australia

ISBN: (数字)9783319226897

ISBN: (纸本)9783319226880

Recent empirical studies in the area of mobile application testing indicate the need for specific testing techniques and methods for mobile applications. This is due to mobile applications being significantly different than traditional web and desktop applications, particularly in terms of the physical constraints of mobile devices and the very different features of their operating systems. In this paper, we presented a multiple case-study involving four software development companies in the area of mobile and smartphones application. We aimed to identify testing techniques currently being applied by developers and challenges that they are facing. Our principle results are that many industrial teams seem to lack sufficient knowledge on how to test mobile applications, particularly in the areas of mobile application life-cycle conformance, context-awareness, and integration testing. We also found that there is no formal testing approach or methodology that can facilitate a development team to systematically test a critical mobile application. © Springer International Publishing Switzerland 2015.

关键词： Integration testing

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Quantum State Tomography via Reduced Density Matrices

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Physical Review Letters 2017年第2期118卷 020401-020401页

作者： Tao Xin Dawei Lu Joel Klassen Nengkun Yu Zhengfeng Ji Jianxin Chen Xian Ma Guilu Long Bei Zeng Raymond Laflamme State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China Institute for Quantum Computing University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Physics University of Guelph Guelph N1G 2W1 Ontario Canada Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney New South Wales 2007 Australia Department of Mathematics and Statistics University of Guelph Guelph N1G 2W1 Ontario Canada State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences 100190 Beijing China Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland USA Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada Canadian Institute for Advanced Research Toronto M5G 1Z8 Ontario Canada Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada

Quantum state tomography via local measurements is an efficient tool for characterizing quantum states. However, it requires that the original global state be uniquely determined (UD) by its local reduced density matrices (RDMs). In this work, we demonstrate for the first time a class of states that are UD by their RDMs under the assumption that the global state is pure, but fail to be UD in the absence of that assumption. This discovery allows us to classify quantum states according to their UD properties, with the requirement that each class be treated distinctly in the practice of simplifying quantum state tomography. Additionally, we experimentally test the feasibility and stability of performing quantum state tomography via the measurement of local RDMs for each class. These theoretical and experimental results demonstrate the advantages and possible pitfalls of quantum state tomography with local measurements.

关键词： Quantum entanglement Quantum information processing Quantum tomography Random matrix theory

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Efficient design space exploration by knowledge transfer

Efficient design space exploration by knowledge transfer

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International Conference on Hardware/software Codesign and System Synthesis (CODES)

作者： Dandan Li Senzhang Wang Shuzhen Yao Yu-Hang Liu Yuanqi Cheng Xian-He Sun School of Computer Science and Engineering Beihang University Beijing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Computer Science Illinois Institute of Technology Chicago USA

ISBN: (纸本)9781509035908

Due to the exponentially increasing size of design space of microprocessors and time-consuming simulations, predictive models have been widely employed in design space exploration (DSE). Traditional approaches mostly build a program-specific predictor that needs a large number of program-specific samples. Thus considerable simulation cost is required for each program. In this paper, we study the novel problem of transferring knowledge from the labeled samples of previous programs to help predict the responses of the new target program whose labeled samples are very sparse. Inspired by the recent advances of transfer learning, we propose a transfer learning based DSE framework TrDSE to build a more efficient and effective predictive model for the target program with only a few simulations by borrowing knowledge from previous programs. Specifically, TrDSE includes two phases: 1) clustering the programs based on the proposed orthogonal array sampling and the distribution related features, and 2) with the guidance of clustering results, predicting the responses of configurations in design space of the target program by a transfer learning based regression algorithm. We evaluate the proposed TrDSE on the benchmarks of SPEC CPU 2006 suite. The results demonstrate that the proposed framework is more efficient and effective than state-of-art DSE techniques.

关键词： Predictive models Knowledge transfer Clustering algorithms Feature extraction Prediction algorithms Training data Space exploration

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Inferring an indeterminate string from a prefix graph

Inferring an indeterminate string from a prefix graph

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作者： Alatabbi, Ali Sohel Rahman, M. Smyth, W.F. Department of Informatics King's College London United Kingdom Department of Computer Science and Engineering Bangladesh University of Engineering and Science Bangladesh Algorithms Research Group Department of Computing and Software McMaster University Canada School of Engineering and Information Technology Murdoch University Australia

An indeterminate string (or, more simply, just a string) x=x[1.n] on an alphabet Σ is a sequence of nonempty subsets of Σ. We say that x[i1] and x[i2] match (written x[i1]≈x[i2]) if and only if x[i1]∩x[i2]∗. A feasible array is an array y=y[1.n] of integers such that y[1]=n and for every i∈2.n, y[i]∈0.n-i+1. A prefix table of a string x is an array π=π[1.n] of integers such that, for every i∈1.n, π[i]=j if and only if x[i.i+j-1] is the longest substring at position i of x that matches a prefix of x. It is known from [6] that every feasible array is a prefix table of some indeterminate string. A prefix graph P=Py is a labelled simple graph whose structure is determined by a feasible array y. In this paper we show, given a feasible array y, how to use Py to construct a lexicographically least indeterminate string on a minimum alphabet whose prefix table π=y. © 2014 Elsevier B.V. All rights reserved.

关键词： Reverse engineering

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Report on the fourth workshop on sustainable software for science: practice and experiences (WSSSPE4)

arXiv

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arXiv 2017年

作者： Katz, Daniel S. Niemeyer, Kyle E. Gesing, Sandra Hwang, Lorraine Bangerth, Wolfgang Hettrick, Simon Idaszak, Ray Salac, Jean Chue Hong, Neil Núñez-Corrales, Santiago Allen, Alice Geiger, R. Stuart Miller, Jonah Chen, Emily Dubey, Anshu Lago, Patricia University of Illinois Urbana–ChampaignIL United States School of Mechanical Industrial and Manufacturing Engineering Oregon State University CorvallisOR United States Center for Research Computing Department of Computer Science and Engineering University of Notre Dame IN United States University of California DavisCA United States Department of Mathematics Colorado State University Fort CollinsCO United States Software Sustainability Institute Southampton United Kingdom RENCI University of North Carolina at Chapel Hill Chapel HillNC United States University of Virginia CharlottesvilleVA United States University of Edinburgh United Kingdom University of Illinois at Urbana-Champaign IL United States Astrophysics Source Code Library Astronomy Department University of Maryland College ParkMD United States BerkeleyCA United States Perimeter Institute for Theoretical Physics Department of Physics University of Guelph GuelphON Canada University of Illinois at Urbana-Champaign United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States University of Chicago ChicagoIL United States Vrije Universiteit Amsterdam Netherlands

This report records and discusses the Fourth Workshop on Sustainable software for science: Practice and Experiences (WSSSPE4). The report includes a description of the keynote presentation of the workshop, the mission and vision statements that were drafted at the workshop and finalized shortly after it, a set of idea papers, position papers, experience papers, demos, and lightning talks, and a panel discussion. The main part of the report covers the set of working groups that formed during the meeting, and for each, discusses the participants, the objective and goal, and how the objective can be reached, along with contact information for readers who may want to join the group. Finally, we present results from a survey of the workshop attendees. Copyright © 2017, The Authors. All rights reserved.

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Let's work together: Fast tag identification by interference elimination for multiple RFID readers

Let's work together: Fast tag identification by interference...

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International Conference on Network Protocols

作者： Xuan Liu Bin Xiao Feng Zhu Shigeng Zhang College of Computer Science and Electronic Engineering Hunan University P. R. China Department of Computing The Hong Kong Polytechnic University Hong Kong State Key Laboratory for Novel Software Technology Nanjing University P.R. China School of Information Science and Engineering Central South University P. R. China

ISBN: (纸本)9781509032822

Fast tag identification is a fundamental challenging problem in multi-reader RFID systems. The challenge is how to effectively handle Reader-Tag (RT) collisions and Reader-Reader (RR) collisions among adjacent readers. These collisions are caused by interfering signals simultaneously transmitted by the readers, and may disallow adjacent readers to work together. Prior works tackle this problem by scheduling adjacent readers to work in different time slots. The readers that are selected to simultaneously work, however, are usually only a small proportion of the total readers. This greatly restricts the identification throughput. Our insightful investigation on the current tag identification protocol reveals that RT collisions are caused by asynchronous actions of readers. i.e., a reader transmits signal while its adjacent readers receive. We thus develop Slot Splitting, a technique that can completely eliminate RT collisions by synchronizing actions of readers. We also propose a reader selection algorithm that minimizes RR collisions by selecting a reader set with maximum reading efficiency. The combination of these new techniques inspires Federal, a Fast and efficient tag identification protocol with interference-elimination-based reader scheduling. To our knowledge, Federal is the first identification protocol that completely eliminates RT collisions and minimizes RR collisions in both regularly and randomly deployed multi-reader systems. We validate the feasibility of Slot Splitting with experiments on the USRP platform and evaluate the performance of Federal through extensive simulations. The results show that Federal can increase tag identification throughput by up to 218% compared with the state-of-the-art work.

关键词： Protocols Throughput Radiofrequency identification Interference Radiation detectors Conferences Monitoring

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Omnidirectional chargability with directional antennas

Omnidirectional chargability with directional antennas

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International Conference on Network Protocols

作者： Haipeng Dai Xiaoyu Wang Alex X. Liu Fengmin Zhang Yang Zhao Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu CHINA Department of Computer Science and Engineering Michigan State University East Lansing MI USA Shanghai key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai CHINA

ISBN: (纸本)9781509032822

Wireless Power Transfer (WPT) has received more and more attentions because of its convenience and reliability. In this paper, we first propose the notion of omnidirectional charging by which an area is omnidirectionally charged if a device with directional antennas at any position in the area with any orientation can be charged by directional chargers with power being no smaller than a given threshold. We present our empirical charging model based on field experimental results using off-the-shelf WPT products. Next, we consider the problem of detecting whether the target area achieves omnidirectional charging given a deterministic deployment of chargers. We develop piecewise constant approximation and area discretization techniques to partition the target area into subareas and approximate powers from chargers as constants. Then we propose the Minimum Coverage Set extraction technique which reduces the continuous search space to a discrete one and thereby allows a fast detection algorithm. Moreover, we consider the problem of determining the probability that the target area achieves omnidirectional charging given a random deployment of chargers. We first replace the target area by grid points on triangular lattices to reduce the search space from infinite to finite, then approximate chargers' power with reasonable relaxation, and derive an upper bound of the omnidirectional charging probability. Finally, we conduct both simulation and field experiments, and the results show that our algorithm outperforms comparison algorithms by at least 120%, and the consistency degree of our theoretical results and field experimental results is larger than 93.6%.

关键词： Wireless sensor networks Wireless communication Directional antennas Adaptation models Joining processes Conferences Protocols

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A Survey of Blue-Noise Sampling and Its Applications

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Journal of Computer science & Technology 2015年第3期30卷 439-452页

作者：严冬明郭建伟王斌张晓鹏 Peter Wonk Visual Computing Center King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Software Tsinghua University Beijing 100084 China Department of Computer Science and Engineering Arizona State University Tempe AZ 85287 U.S.A.

In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorithms based on various aspects, e.g., the sampling domain and the type of algorithm. We demonstrate several well-known applications that can be improved by recent blue-noise sampling techniques, as well as some new applications such as dynamic sampling and blue-noise remeshing.

关键词： blue-noise sampling Poisson-disk sampling Lloyd relaxation rendering remeshing

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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The 5th International Conference on Biomedical engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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