Artículos de revistas: "Computer software Verification"

1

Goerigk, Wolfgang. "Mechanical Software Verification." Electronic Notes in Theoretical Computer Science 58, no. 2 (November 2001): 117–37. http://dx.doi.org/10.1016/s1571-0661(04)00282-8.

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2

Kwiatkowska, Marta. "From software verification to ‘everyware’ verification." Computer Science - Research and Development 28, no. 4 (September 7, 2013): 295–310. http://dx.doi.org/10.1007/s00450-013-0249-1.

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3

Dobrescu, Mihai, and Katerina Argyraki. "Software dataplane verification." Communications of the ACM 58, no. 11 (October 23, 2015): 113–21. http://dx.doi.org/10.1145/2823400.

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4

Malkis, Alexander, and Anindya Banerjee. "Verification of software barriers." ACM SIGPLAN Notices 47, no. 8 (September 11, 2012): 313–14. http://dx.doi.org/10.1145/2370036.2145871.

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5

Halpern, J. D., S. Owre, N. Proctor, and W. F. Wilson. "Muse—A Computer Assisted Verification System." IEEE Transactions on Software Engineering SE-13, no. 2 (February 1987): 151–56. http://dx.doi.org/10.1109/tse.1987.226477.

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6

Flanagan, Cormac, and Shaz Qadeer. "Predicate abstraction for software verification." ACM SIGPLAN Notices 37, no. 1 (January 2002): 191–202. http://dx.doi.org/10.1145/565816.503291.

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7

Greengard, Samuel. "Formal software verification measures up." Communications of the ACM 64, no. 7 (July 2021): 13–15. http://dx.doi.org/10.1145/3464933.

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8

Andersen, B. Scott, and George Romanski. "Verification of safety-critical software." Communications of the ACM 54, no. 10 (October 2011): 52–57. http://dx.doi.org/10.1145/2001269.2001286.

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9

Andersen, B. Scott, and George Romanski. "Verification of Safety-critical Software." Queue 9, no. 8 (August 2011): 50–59. http://dx.doi.org/10.1145/2016036.2024356.

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10

Hailpern, B., and P. Santhanam. "Software debugging, testing, and verification." IBM Systems Journal 41, no. 1 (2002): 4–12. http://dx.doi.org/10.1147/sj.411.0004.

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11

Wang, Shihao. "Software Simulation for Hardware/Software Co-Verification." Journal of Computer Research and Development 42, no. 3 (2005): 514. http://dx.doi.org/10.1360/crad20050322.

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12

Xu, Jian, Xinai Zhang, Yi Zhao, and Bing Xu. "Verification of Air Data Computer Software using Formal Methods." Journal of Physics: Conference Series 1827, no. 1 (March 1, 2021): 012207. http://dx.doi.org/10.1088/1742-6596/1827/1/012207.

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13

Krämer, Bernd, and Wolfgang Halang. "Computer-Aided Specification and Verification of Process Control Software." IFAC Proceedings Volumes 25, no. 30 (October 1992): 7–12. http://dx.doi.org/10.1016/s1474-6670(17)49399-2.

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14

Ding, Zuohua, and Jing Liu. "An Improvement of Software Architecture Verification." Electronic Notes in Theoretical Computer Science 243 (July 2009): 49–67. http://dx.doi.org/10.1016/j.entcs.2009.07.005.

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15

Gotlieb, Arnaud. "TCAS software verification using constraint programming." Knowledge Engineering Review 27, no. 3 (July 26, 2012): 343–60. http://dx.doi.org/10.1017/s0269888912000252.

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AbstractSafety-critical software must be thoroughly verified before being exploited in commercial applications. In particular, any TCAS (Traffic Alert and Collision Avoidance System) implementation must be verified against safety properties extracted from the anti-collision theory that regulates the controlled airspace. This verification step is currently realized with manual code reviews and testing. In our work, we explore the capabilities of Constraint Programming for automated software verification and testing. We built a dedicated constraint solving procedure that combines constraint propagation with Linear Programming to solve conditional disjunctive constraint systems over bounded integers extracted from computer programs and safety properties. An experience we made on verifying a publicly available TCAS component implementation against a set of safety-critical properties showed that this approach is viable and efficient.

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16

Kishi, Tomoji, and Natsuko Noda. "Formal verification and software product lines." Communications of the ACM 49, no. 12 (December 2006): 73–77. http://dx.doi.org/10.1145/1183236.1183270.

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17

Guo, Yinghua, Jill Slay, and Jason Beckett. "Validation and verification of computer forensic software tools—Searching Function." Digital Investigation 6 (September 2009): S12—S22. http://dx.doi.org/10.1016/j.diin.2009.06.015.

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18

Abadi, Martín, and Bruno Blanchet. "Computer-assisted verification of a protocol for certified email." Science of Computer Programming 58, no. 1-2 (October 2005): 3–27. http://dx.doi.org/10.1016/j.scico.2005.02.002.

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19

Khanna, S. "Logic Programming for Software Verification and Testing." Computer Journal 34, no. 4 (April 1, 1991): 350–57. http://dx.doi.org/10.1093/comjnl/34.4.350.

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20

Benoit, Anne, Saurabh K. Raina, and Yves Robert. "Efficient checkpoint/verification patterns." International Journal of High Performance Computing Applications 31, no. 1 (July 28, 2016): 52–65. http://dx.doi.org/10.1177/1094342015594531.

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Errors have become a critical problem for high-performance computing. Checkpointing protocols are often used for error recovery after fail-stop failures. However, silent errors cannot be ignored, and their peculiarity is that such errors are identified only when the corrupted data is activated. To cope with silent errors, we need a verification mechanism to check whether the application state is correct. Checkpoints should be supplemented with verifications to detect silent errors. When a verification is successful, only the last checkpoint needs to be kept in memory because it is known to be correct. In this paper, we analytically determine the best balance of verifications and checkpoints so as to optimize platform throughput. We introduce a balanced algorithm using a pattern with p checkpoints and q verifications, which regularly interleaves both checkpoints and verifications across same-size computational chunks. We show how to compute the waste of an arbitrary pattern, and we prove that the balanced algorithm is optimal when the platform MTBF (mean time between failures) is large in front of the other parameters (checkpointing, verification and recovery costs). We conduct several simulations to show the gain achieved by this balanced algorithm for well-chosen values of p and q, compared with the base algorithm that always perform a verification just before taking a checkpoint ( p = q = 1), and we exhibit gains of up to 19%.

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21

Min, Byungho, and Vijay Varadharajan. "Rethinking Software Component Security: Software Component Level Integrity and Cross Verification." Computer Journal 59, no. 11 (August 10, 2016): 1735–48. http://dx.doi.org/10.1093/comjnl/bxw047.

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22

Kajiwara, M., M. Itoh, and H. Ichikawa. "Specification and verification technologies for communication software." IEEE Communications Magazine 23, no. 8 (August 1985): 15–25. http://dx.doi.org/10.1109/mcom.1985.1092633.

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23

Thüm, Thomas, Ina Schaefer, Sven Apel, and Martin Hentschel. "Family-based deductive verification of software product lines." ACM SIGPLAN Notices 48, no. 3 (April 10, 2013): 11–20. http://dx.doi.org/10.1145/2480361.2371404.

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24

Li, Shao Feng. "A Study on Network Protocol Validation Based on Timed Automata." Applied Mechanics and Materials 543-547 (March 2014): 3386–90. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3386.

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With the increasingly complex of computer software system, traditional software engineering methods for major software development will inevitably produce a lot of mistakes and catastrophic consequences for key industry users. Experiment with software engineering methods cannot guarantee the behavior at infinity reliability and security of the state space. All this requires formal analysis and verification to the complex system. In protocol verification based on automatic machines, the automaton is used to represent the behavior of the system, the time automaton is a formal method can be well applied to the network protocol verification.

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25

Ozkaya, Mert. "Formal Verification of Contractual Software Architectures using SPIN." Malaysian Journal of Computer Science 28, no. 4 (December 1, 2015): 318–37. http://dx.doi.org/10.22452/mjcs.vol28no4.4.

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26

Calinescu, Radu, Carlo Ghezzi, Marta Kwiatkowska, and Raffaela Mirandola. "Self-adaptive software needs quantitative verification at runtime." Communications of the ACM 55, no. 9 (September 2012): 69–77. http://dx.doi.org/10.1145/2330667.2330686.

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27

Sacha, Krzysztof. "Verification and implementation of software for dependable controllers." International Journal of Critical Computer-Based Systems 1, no. 1/2/3 (2010): 238. http://dx.doi.org/10.1504/ijccbs.2010.031717.

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28

Gagliardi, John. "Medical Device Software: Verification, Validation and Compliance." Biomedical Instrumentation & Technology 45, no. 2 (March 1, 2011): 95. http://dx.doi.org/10.2345/0899-8205-45.2.95.

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29

Dyer, M., and A. Kouchakdjian. "Correctness verification: alternative to structural software testing." Information and Software Technology 32, no. 1 (January 1990): 53–59. http://dx.doi.org/10.1016/0950-5849(90)90046-t.

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30

He, Chaobing. "Verification of Several Important Theorems in Simple Random Sampling Using R Software." Journal of Advance Research in Mathematics And Statistics (ISSN: 2208-2409) 8, no. 12 (December 31, 2021): 01–07. http://dx.doi.org/10.53555/nnms.v8i12.1134.

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This paper considers the verification of several important theorems in simple random sampling using R software. First several important theorems in simple random sampling are introduced systematically. Then computer program for the verification of these theorems is written using R. According to these R codes, the paper verifies these theorems. The output proves that the R codes are very practical and effective.

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31

Elqortobi, Mounia, Warda El-Khouly, Amine Rahj, Jamal Bentahar, and Rachida Dssouli. "Verification and testing of safety-critical airborne systems: A model-based methodology." Computer Science and Information Systems 17, no. 1 (2020): 271–92. http://dx.doi.org/10.2298/csis190430040e.

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In this paper, we address the issues of safety-critical software verification and testing that are key requirements for achieving DO-178C and DO- 331 regulatory compliance for airborne systems. Formal verification and testing are considered two different activities within airborne standards and they belong to two different levels in the avionics software development cycle. The objective is to integrate model-based verification and model-based testing within a single framework and to capture the benefits of their cross-fertilization. This is achieved by proposing a new methodology for the verification and testing of parallel communicating agents based on formal models. In this work, properties are extracted from requirements and formally verified at the design level, while the verified properties are propagated to the implementation level and checked via testing. The contributions of this paper are a methodology that integrates verification and testing, formal verification of some safety critical software properties, and a testing method for Modified Condition/Decision Coverage (MC/DC). The results of formal verification and testing can be used as evidence for avionics software certification.

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32

Cao, Zongyu, Wanyou Lv, Yanhong Huang, Jianqi Shi, and Qin Li. "Formal Analysis and Verification of Airborne Software Based on DO-333." Electronics 9, no. 2 (February 14, 2020): 327. http://dx.doi.org/10.3390/electronics9020327.

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With rapid technological advances in airborne control systems, it has become imperative to ensure the reliability, robustness, and adaptability of airborne software since failure of these software could result in catastrophic loss of property and life. DO-333 is a supplement to the DO-178C standard, which is dedicated to guiding the application of formal methods in the review and analysis of airborne software development processes. However, DO-333 lacks theoretical guidance on how to choose appropriate formal methods and tools to achieve verification objectives at each stage of the verification process, thereby limiting their practical application. This paper is intended to illustrate the formal methods and tools available in the verification process to lay down a general guide for the formal development and verification of airborne software. We utilized the Air Data Computer (ADC) software as the research object and applied different formal methods to verify software lifecycle artifacts. This example explains how to apply formal methods in practical applications and proves the effectiveness of formal methods in the verification of airborne software.

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33

Ivančić, Franjo, Sriram Sankaranarayanan, and Chao Wang. "Foreword: Special issue on numerical software verification." Formal Methods in System Design 35, no. 3 (December 2009): 227–28. http://dx.doi.org/10.1007/s10703-009-0090-0.

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34

Chaki, Sagar, Edmund Clarke, Joël Ouaknine, Natasha Sharygina, and Nishant Sinha. "Concurrent software verification with states, events, and deadlocks." Formal Aspects of Computing 17, no. 4 (September 21, 2005): 461–83. http://dx.doi.org/10.1007/s00165-005-0071-z.

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35

Hinsen, Konrad. "Verifiability in computer-aided research: the role of digital scientific notations at the human-computer interface." PeerJ Computer Science 4 (July 23, 2018): e158. http://dx.doi.org/10.7717/peerj-cs.158.

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Most of today’s scientific research relies on computers and software for processing scientific information. Examples of such computer-aided research are the analysis of experimental data or the simulation of phenomena based on theoretical models. With the rapid increase of computational power, scientific software has integrated more and more complex scientific knowledge in a black-box fashion. As a consequence, its users do not know, and do not even have a chance of finding out, which assumptions and approximations their computations are based on. This black-box nature of scientific software has made the verification of much computer-aided research close to impossible. The present work starts with an analysis of this situation from the point of view of human-computer interaction in scientific research. It identifies the key role of digital scientific notations at the human-computer interface, reviews the most popular ones in use today, and describes a proof-of-concept implementation of Leibniz, a language designed as a verifiable digital scientific notation for models formulated as mathematical equations.

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36

Zhang, Xingjun, Yan Yang, Endong Wang, Ilsun You, and Xiaoshe Dong. "Modelling software fault management with runtime verification." International Journal of Ad Hoc and Ubiquitous Computing 20, no. 1 (2015): 26. http://dx.doi.org/10.1504/ijahuc.2015.071660.

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37

Rehman, Waqas Ur, Muhammad Sohaib Ayub, and Junaid Haroon Siddiqui. "Verification of MPI Java programs using software model checking." ACM SIGPLAN Notices 51, no. 8 (November 9, 2016): 1–2. http://dx.doi.org/10.1145/3016078.2851192.

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38

Murrill, Branson W. "Integrating Software Analysis, Testing, and Verification into the Undergraduate Computer Science Curriculum." Computer Science Education 8, no. 2 (August 1998): 85–99. http://dx.doi.org/10.1076/csed.8.2.85.3819.

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39

Staskauskas, M. G. "An experience in the formal verification of industrial software." Communications of the ACM 39, no. 12es (December 1996): 256. http://dx.doi.org/10.1145/272682.272719.

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40

Revesz, Peter Z., and Robert J. Woodward. "Estimating the maximum rise in temperature according to climate models using abstract interpretation." Acta Universitatis Sapientiae, Informatica 11, no. 1 (August 1, 2019): 5–23. http://dx.doi.org/10.2478/ausi-2019-0001.

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Abstract Current climate models are complex computer programs that are typically iterated time-step by time-step to predict the next set of values of the climate-related variables. Since these iterative methods are necessarily computed only for a fixed number of iterations, they are unable to answer the natural question whether there is a limit to the rise of global temperature. In order to answer that question we propose to combine climate models with software verification techniques that can find invariant conditions for the set of program variables. In particular, we apply the constraint database approach to software verification to find that the rise in global temperature is bounded according to the common Java Climate Model that implements the Wigley/Raper Upwelling-Diffusion Energy Balance Model climate model.

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41

Curzon, Paul, Rimvydas Rukšėnas, and Ann Blandford. "An approach to formal verification of human–computer interaction." Formal Aspects of Computing 19, no. 4 (June 2, 2007): 513–50. http://dx.doi.org/10.1007/s00165-007-0035-6.

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42

Wang, Xiaoyi, Tianyang Yao, and Zhaoyao Shi. "Calibration Method Based on Virtual Gear Artefact for Computer Vision Measuring Instrument of Fine Pitch Gear." Sensors 24, no. 7 (April 3, 2024): 2289. http://dx.doi.org/10.3390/s24072289.

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The verification of the correctness, adaptability, and robustness of software systems in modern precision measurement instruments is of great significance. Due to the difficulty in processing and calibrating high-precision fine-pitch gear artefacts, the function verification and accuracy calibration of vision measurement instruments for the fine-pitch gear have become a challenge. The calibration method of the gear vision measurement system based on the virtual gear artefact involves two steps, namely obtaining and applying the virtual artefact. The obtained virtual gear artefact has the same geometric features, error features, and image edge features as the real artefact. The calibration method based on the virtual artefact can complete the correctness verification of the gear vision measurement system, and is superior to the traditional methods in adaptability verification, robustness verification, and fault analysis. In a test, the characteristic error of the virtual gear artefact could be reproduced with the original shape in the evaluation results of the computer vision gear measurement (CVGM) system, while the reproduction error did not exceed 1.9 μm. This can meet the requirements of the verification of the gear vision measurement software. The application of the virtual gear artefact can significantly improve the accuracy and robustness of the computer vision measuring instrument of the fine-pitch gear.

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43

Muñoz-Quijada, Maria, Luis Sanz, and Hipolito Guzman-Miranda. "SW-VHDL Co-Verification Environment Using Open Source Tools." Electronics 9, no. 12 (December 10, 2020): 2104. http://dx.doi.org/10.3390/electronics9122104.

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The verification of complex digital designs often involves the use of expensive simulators. The present paper proposes an approach to verify a specific family of complex hardware/software systems, whose hardware part, running on an FPGA, communicates with a software counterpart executed on an external processor, such as a user/operator software running on an external PC. The hardware is described in VHDL and the software may be described in any computer language that can be interpreted or compiled into a (Linux) executable file. The presented approach uses open source tools, avoiding expensive license costs and usage restrictions.

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44

NADOLSKI, V. "VERIFICATION AND VALIDATION OF A COMPUTER COMPUTATIONAL MODEL FOR THE DESIGN OF BUILDING STRUCTURES." Herald of Polotsk State University. Series F. Civil engineering. Applied sciences, no. 2 (June 28, 2024): 42–50. http://dx.doi.org/10.52928/2070-1683-2024-37-2-42-50.

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A review and analysis of the verification and validation procedures of computer computational models has been performed in order to provide a conceptual framework and guidance on their implementation in relation to the design of building structures. The description of verification procedures for computer software and computer computational models is presented. The main stages of validation are formulated. The purpose of validation is to confirm the applicability, predictive ability and determination of the characteristics of the accuracy of computer models. Based on the analysis of the design value of the load-bearing capacity, a conclusion is made about the number of experiments required for validation for computer models. The study focuses on the description of verification and validation procedures for computer models of new design solutions and non-standardized model parameters. However, the recommendations given here are also suitable for more studied design solutions, while the scale of verification and validation activities may be reduced.

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45

Satin, Lukáš, and Jozef Bílik. "Verification CAE System for Plastic Injection." Applied Mechanics and Materials 834 (April 2016): 79–83. http://dx.doi.org/10.4028/www.scientific.net/amm.834.79.

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This article is focused on the field of computer simulation and it is subsequent verification in practice. The work highlights the injection process, the simulation software that is specialized in injection molding and the technology process of injection itself. The major subject of the thesis is the use of the computer aided injection molding technology by using the CAE systems. The experimental part of the thesis deals with the production of the 3D model specific plastic parts in two modifications, injection molding simulation in the system Moldex3D and digitization of moldings on the optical 3D scanner. In the thesis we also provide measuring realization on digitized models and comparison of the parts size with the computer model. In conclusion we summarize the results achieved from the comparison. The thesis is carried out in cooperation with the Simulpast s.r.o.

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46

Ray, Arnab, Raoul Jetley, Paul L. Jones, and Yi Zhang. "Model-Based Engineering for Medical-Device Software." Biomedical Instrumentation & Technology 44, no. 6 (November 1, 2010): 507–18. http://dx.doi.org/10.2345/0899-8205-44.6.507.

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Abstract This paper demonstrates the benefits of adopting model-based design techniques for engineering medical device software. By using a patient-controlled analgesic (PCA) infusion pump as a candidate medical device, the authors show how using models to capture design information allows for i) fast and efficient construction of executable device prototypes ii) creation of a standard, reusable baseline software architecture for a particular device family, iii) formal verification of the design against safety requirements, and iv) creation of a safety framework that reduces verification costs for future versions of the device software.1

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47

Parizek, P., and F. Plasil. "Assume-guarantee verification of software components in SOFA 2 framework." IET Software 4, no. 3 (2010): 210. http://dx.doi.org/10.1049/iet-sen.2009.0016.

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48

Zhang, Min, Kazuhiro Ogata, and Kokichi Futatsugi. "Formalization and Verification of Behavioral Correctness of Dynamic Software Updates." Electronic Notes in Theoretical Computer Science 294 (March 2013): 12–23. http://dx.doi.org/10.1016/j.entcs.2013.02.013.

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49

Ferreira, Nelson Guimarães, and Paulo Sérgio Muniz Silva. "Automatic Verification of Safety Rules for a Subway Control Software." Electronic Notes in Theoretical Computer Science 130 (May 2005): 323–43. http://dx.doi.org/10.1016/j.entcs.2005.03.017.

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50

Feinerer, Ingo, and Gernot Salzer. "A comparison of tools for teaching formal software verification." Formal Aspects of Computing 21, no. 3 (June 11, 2008): 293–301. http://dx.doi.org/10.1007/s00165-008-0084-5.

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Artículos de revistas sobre el tema "Computer software Verification"

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