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arXiv 2018年

作者： López-Martín, Cuauhtémoc Azzeh, Mohammad Nassif, Ali Bou Banitaan, Shadi Department of Information Systems Universidad de Guadalajara Mexico Department of Software Engineering Applied Science Private University Amman Jordan Department of Electrical and Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States

An important product measure to determine the effectiveness of software processes is the defect density (DD). In this study, we propose the application of support vector regression (SVR) to predict the DD of new software projects obtained from the International software Benchmarking Standards Group (ISBSG) Release 2018 data set. Two types of SVR (i.e., Ε-SVR and ν-SVR) were applied to train and test these projects. Each SVR used four types of kernels. The prediction accuracy of each SVR was compared to that of a statistical regression (i.e., a simple linear regression, SLR). Statistical significance test showed that ν-SVR with polynomial kernel was better than that of SLR when new software projects were developed on mainframes and coded in programming languages of third generation. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Generation and application of constrained interaction test suites using base forbidden tuples with mixed neighborhood tabu search

arXiv

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arXiv 2019年

作者： Hasan, Imad H. Ahmed, Bestoun S. Potrus, Moayad Y. Zamli, Kamal Z. Erbil44002 Iraq Department of Mathematics and Computer Science Karlstad University Karlstad651 88 Sweden Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Gambang Pahang26300 Malaysia

To ensure the quality of current highly configurable software systems, intensive testing is needed to test all the configuration combinations and detect all the possible faults. This task becomes more challenging for most modern software systems when constraints are given for the configurations. Here, intensive testing is almost impossible, especially considering the additional computation required to resolve the constraints during the test generation process. In addition, this testing process is exhaustive and time-consuming. Combinatorial interaction strategies can systematically reduce the number of test cases to construct a minimal test suite without affecting the effectiveness of the tests. This paper presents a new efficient search-based strategy to generate constrained interaction test suites to cover all possible combinations. The paper also shows a new application of constrained interaction testing in software fault searches. The proposed strategy initially generates the set of all possible t − tuple combinations;then, it filters out the set by removing the forbidden t − tuples using the base forbidden tuple (BFT) approach. The strategy also utilizes a mixed neighborhood tabu search (TS) to construct optimal or near-optimal constrained test suites. The efficiency of the proposed method is evaluated through a comparison against two well-known state-of-the-art tools. The evaluation consists of three sets of experiments for 35 standard benchmarks. Additionally, the effectiveness and quality of the results are assessed using a real-world case study. Experimental results show that the proposed strategy outperforms one of the competitive strategies, ACTS, for approximately 83% of the benchmarks and achieves similar results to CASA for 65% of the benchmarks when the interaction strength is 2. For an interaction strength of 3, the proposed method outperforms other competitive strategies for approximately 60% and 42% of the benchmarks. The proposed strategy can also

关键词： software testing

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Comparative analysis of time series model and machine testing systems for crime forecasting

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Neural Computing and Applications 2020年第17期33卷 10621-10636页

作者： Jha, Sudan Yang, Eunmok Almagrabi, Alaa Omran Bashir, Ali Kashif Joshi, Gyanendra Prasad School of Computer Science and Engineering Lovely Professional University Phagwara India School of Software Soongsil University Seoul South Korea Department of Information Systems King Abdul Aziz University Jeddah Saudi Arabia Department of Computing and Mathematics Manchester Metropolitan University Manchester UK School of Electrical Engineering and Computer Science NUST Islamabad Pakistan Department of Computer Science and Engineering Sejong University Seoul South Korea

Crime forecasting has been one of the most complex challenges in law enforcement today, especially when an analysis tends to evaluate inferable and expanded crime rates, although a few methodologies for subsequent equivalents have been embraced before. In this work, we use a strategy for a time series model and machine testing systems for crime estimation. The paper centers on determining the quantity of crimes. Considering various experimental analyses, this investigation additionally features results obtained from a neural system that could be a significant alternative to machine learning and ordinary stochastic techniques. In this paper, we applied various techniques to forecast the number of possible crimes in the next 5 years. First, we used the existing machine learning techniques to predict the number of crimes. Second, we proposed two approaches, a modified autoregressive integrated moving average model and a modified artificial neural network model. The prime objective of this work is to compare the applicability of a univariate time series model against that of a variate time series model for crime forecasting. More than two million datasets are trained and tested. After rigorous experimental results and analysis are generated, the paper concludes that using a variate time series model yields better forecasting results than the predicted values from existing techniques. These results show that the proposed method outperforms existing methods.

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Algorithms for determining semantic relations of formal concepts by cognitive machine learning based on concept algebra

Algorithms for determining semantic relations of formal conc...

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IEEE International Conference on Cognitive Informatics

作者： Mehrdad Valipour Yingxu Wang Omar A. Zatarain Marina L. Gavrilova Denotational Mathematics Software Science and Cognitive Systems Dept. of Electrical and Computer Engineering University of Calgary Calgary Alberta Canada Information Systems Lab Packard Dept. of Electrical Engineering Stanford University Stanford CA USA

ISBN: (纸本)9781509038473

It is recognized that the semantic space of knowledge is a hierarchical concept network. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic relations via machine learning based on concept algebra. The equivalence between formal concepts are analyzed by an Algorithm of Concept Equivalence Analysis (ACEA), which quantitatively determines the semantic similarity of an arbitrary pair of formal concepts. This leads to the development of the Algorithm of Relational Semantic Classification (ARSC) for hierarchically classify any given concept in the semantic space of knowledge. Experiments applying Algorithms ACEA and ARSC on 20 formal concepts are successfully conducted, which encouragingly demonstrate the deep machine understanding of semantic relations and their quantitative weights beyond human perspectives on knowledge learning and natural language processing.

关键词： Writing Instruments Decision support systems Ink Animals Handheld computers Lead

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Scenario-Assisted Deep Reinforcement Learning

arXiv

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arXiv 2022年

作者： Yerushalmi, Raz Amir, Guy Elyasaf, Achiya Harel, David Katz, Guy Marron, Assaf Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot76100 Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Givat Ram Jerusalem91904 Israel Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel

Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements posed by human engineers. In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (specifically, its reward calculation), in a way that allows human engineers to directly contribute their expert knowledge, making the agent under training more likely to comply with various relevant constraints. Moreover, our proposed approach allows formulating these constraints using advanced model engineering techniques, such as scenario-based modeling. This mix of black-box learning-based tools with classical modeling approaches could produce systems that are effective and efficient, but are also more transparent and maintainable. We evaluated our technique using a case-study from the domain of internet congestion control, obtaining promising results. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Dynamical Sampling with Additive Random Noise

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Sampling Theory, Signal Processing, and Data Analysis 2018年第2期17卷 153-182页

作者： Akram Aldroubi Longxiu Huang Ilya Krishtal Akos Ledeczi Roy R. Lederman Peter Volgyesi Department of Mathematics Vanderbilt University Nashville USA Department of Mathematics Northern Illinois University DeKalb USA Department of Electrical Engineering and Computer Science Vanderbilt University Nashville USA Department of Statistics and Data Science Yale University New Haven USA Institute for Software Integrated Systems Vanderbilt University Nashville USA

Dynamical sampling deals with signals that evolve in time under the action of a linear operator. The purpose of the present paper is to analyze the performance of the basic dynamical sampling algorithms in the finite dimensional case and study the impact of additive noise. The algorithms are implemented and tested on synthetic and real data sets, and denoising techniques are integrated to mitigate the effect of the noise. We also develop theoretical and numerical results that validate the algorithm for recovering the driving operators, which are defined via a real symmetric convolution.

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Towards socially sophisticated BDI agents

Towards socially sophisticated BDI agents

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International Conference on MultiAgent systems Proceedings

作者： F. Dignum D. Morley E.A. Sonenberg L. Cavedon Department of Mathematics and Computer Science Eindhovan University of Technology Eindhoven Netherlands Department of Computer Science and Software Engineering University of Melbourne Parkville VIC Australia Department of Information Systems University of Melbourne Parkville VIC Australia Department of Computer Science RMIT University Melbourne VIC Australia

We present an approach to social reasoning that integrates prior work on norms and obligations with the BDI approach to agent architectures. Norms and obligations can be used to increase the efficiency of agent reasoning, and their explicit representation supports reasoning about a wide range of behaviour types in a single framework. We propose a modified BDI interpreter loop that takes norms and obligations into account in an agent's deliberation.

关键词： computer science Multiagent systems mathematics software engineering Information systems Collaborative work Humans Stability

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AMOGA: A Static-Dynamic Model Generation Strategy for Mobile Apps Testing

arXiv

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arXiv 2019年

作者： Salihu, Ibrahim-Anka Ibrahim, Rosziati Ahmed, Bestoun S. Zamli, Kamal Z. Usman, Asmau Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor86400 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor86400 Malaysia Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang GambangPahang26300 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor86400 Malaysia

In the past few years, mobile devices have been increasingly replacing traditional computers as their capabilities such as CPU computation, memory, RAM size, and many more, are being enhanced almost to the level of conventional computers. These capabilities are being exploited by mobile apps developers to produce apps that offer more functionalities and optimized performance. To ensure acceptable quality and to meet their specifications (e.g., design), mobile apps need to be tested thoroughly. As the testing process is often tedious, test automation can be the key to alleviating such laborious activities. In the context of the Android-based mobile apps, researchers and practitioners have proposed many approaches to automate the testing process mainly on the creation of the test suite. Although useful, most existing approaches rely on reverse engineering a model of the application under test for test case creation. Often, such approaches exhibit a lack of comprehensiveness as the application model does not capture the dynamic behavior of the applications extensively due to the incompleteness of reverse engineering approaches. To address this issue, this paper proposes AMOGA, a strategy that uses a hybrid, static-dynamic approach for generating user interface model from mobile apps for model-based testing. AMOGA implements a novel crawling technique that uses the event list of UI element associated with each event to dynamically exercise the events ordering at the run-time to explore the applications’ behavior. An experimental evaluation was performed to assess the effectiveness of our strategy by measuring the code coverage and the fault detection capability through the use of mutation testing concept. Results of the experimental assessment showed that AMOGA represents an alternative approach for model-based testing of mobile apps by generating comprehensive models to improve the coverage of the applications. The strategy proved its effectiveness by achieving high co

关键词： Model checking

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Physical origins of ruled surfaces on the reduced density matrices geometry

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science China(Physics,Mechanics & Astronomy) 2017年第2期60卷 1-8页

作者： Ji-Yao Chen Zhengfeng Ji Zheng-Xin Liu Xiaofei Qi Nengkun Yu Bei Zeng Duanlu Zhou State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing 100084 China Perimeter Institute for Theoretical Physics Waterloo N2L 2Y5 Canada Centre for Quantum Computation ＆ Intelligent Systems School of Software Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China Department of Physics Renmin University of China Beijing 100872 China Department of Mathematics Shanxi University Taiyuan 030000 China Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Canada Department of Mathematics ＆ Statistics University of Guelph Guelph N1G 2W1 Canada Institute of Physics Chinese Academy of Sciences Beijing 100190 China

The reduced density matrices （RDMs） of many-body quantum states form a convex set. The boundary of low dimensional projections of this convex set may exhibit nontrivial geometry such as ruled surfaces. In this paper, we study the physical origins of these ruled surfaces for bosonic systems. The emergence of ruled surfaces was recently proposed as signatures of symmetry- breaking phase. We show that, apart from being signatures of symmetry-brealdng, ruled surfaces can also be the consequence of gapless quantum systems by demonstrating an explicit example in terms of a two-mode Ising model. Our analysis was largely simplified by the quantum de Finetti＇s theorem--in the limit of large system size, these RDMs are the convex set of all the symmetric separable states. To distinguish ruled surfaces originated from gapless systems from those caused by symmetry- breaking, we propose to use the finite size scaling method for the corresponding geometry. This method is then applied to the two-mode XY model, successfully identifying a ruled surface as the consequence of gapless systems.

关键词： ruled surfaces reduced density matrices bosonic system geometry

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Automated detection of performance regressions: the mono experience

Automated detection of performance regressions: the mono exp...

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International Symposium on Modeling, Analysis and Simulation of computer and Telecommunication systems (MASCOTS)

作者： T. Kalibera L. Bulej P. Tuma Distributed Systems Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Institute of Computer Science Academy of Sciences of Czech Republic Czech Republic

engineering a large software project involves tracking the impact of development and maintenance changes on the software performance. An approach for tracking the impact is regression benchmarking, which involves automated benchmarking and evaluation of performance at regular intervals. Regression benchmarking must tackle the nondeterminism inherent to contemporary computer systems and execution environments and the impact of the nondeterminism on the results. On the example of a fully automated regression benchmarking environment for the mono open-source project, we show how the problems associated with nondeterminism can be tackled using statistical methods.

关键词： MONOS devices Benchmark testing software performance software testing software quality Quality assurance Automatic testing Robustness Performance analysis software engineering

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