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Journal articles on the topic 'Component software – Reliability'

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Published: 4 June 2021

Last updated: 18 February 2022

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1

S, R. Chinnaiyan, and Somasundaram . "Reliability of Component Based Software with Similar Software Components � a Review." i-manager's Journal on Software Engineering 5, no. 2 (December 15, 2010): 44–49. http://dx.doi.org/10.26634/jse.5.2.1335.

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2

ZHOU, Na-qin. "Reliability model of component-based software." Journal of Computer Applications 28, no. 6 (June 20, 2008): 1630–31. http://dx.doi.org/10.3724/sp.j.1087.2008.01630.

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3

Shuchen, Liu, and Cao Wei. "Research of Component Software Reliability Estimation." Open Cybernetics & Systemics Journal 9, no. 1 (April 17, 2015): 199–203. http://dx.doi.org/10.2174/1874110x01509010199.

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4

KRISTIANSEN, MONICA, RUNE WINTHER, and BENT NATVIG. "ON COMPONENT DEPENDENCIES IN COMPOUND SOFTWARE." International Journal of Reliability, Quality and Safety Engineering 17, no. 05 (October 2010): 465–93. http://dx.doi.org/10.1142/s0218539310003895.

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Predicting the reliability of software systems based on a component approach is inherently difficult, in particular due to failure dependencies between the software components. Since it is practically difficult to include all component dependencies in a system's reliability calculation, a more viable approach would be to include only those dependencies that have a significant impact on the assessed system reliability. This paper starts out by defining two new concepts: data-serial and data-parallel components. Then, this paper illustrates how the components' marginal reliabilities put direct restrictions on the components' conditional probabilities, and proves that the degrees of freedom are much fewer than first anticipated when it comes to conditional probabilities. At last, a test system, consisting of five components, is investigated to identify possible rules for selecting the most important component dependencies. To do this, three different techniques are applied: (1) direct calculation, (2) Birnbaum's measure and (3) Principal Component Analysis (PCA). The results from the analyses clearly show that including partial dependency information may give substantial improvements in the reliability predictions, compared to assuming independence between all software components. The analyses also indicate that including only dependencies between data-parallel components may give predictions close to the system's true failure probability, as long as the dependency between the most unreliable components is included. Including only dependencies between data-serial components may however result in predictions even worse than by assuming independence between all software components.

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5

Tomar, Dimpal, and Pradeep Tomar. "New component-based reliability model to predict the reliability of component-based software." International Journal of Reliability and Safety 13, no. 1/2 (2019): 83. http://dx.doi.org/10.1504/ijrs.2019.097018.

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6

Tomar, Dimpal, and Pradeep Tomar. "New component-based reliability model to predict the reliability of component-based software." International Journal of Reliability and Safety 13, no. 1/2 (2019): 83. http://dx.doi.org/10.1504/ijrs.2019.10017892.

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7

Yakovyna, Vitalij S. "COMPONENT BASED HIGH-ORDER SOFTWARE RELIABILITY MODELS." ELECTRICAL AND COMPUTER SYSTEMS 19, no. 95 (July 2, 2015): 252–55. http://dx.doi.org/10.15276/eltecs.19.95.2015.57.

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8

HOU, Chun-Yan, Gang CUI, and Hong-Wei LIU. "Rate-Based Component Software Reliability Process Simulation." Journal of Software 22, no. 11 (November 14, 2011): 2749–59. http://dx.doi.org/10.3724/sp.j.1001.2011.03930.

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9

Reussner, Ralf H., Heinz W. Schmidt, and Iman H. Poernomo. "Reliability prediction for component-based software architectures." Journal of Systems and Software 66, no. 3 (June 2003): 241–52. http://dx.doi.org/10.1016/s0164-1212(02)00080-8.

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10

Wang, Qiang, Yang Lu, Zijun Xu, and Jianghong Han. "Software reliability model for component interaction mode." Journal of Electronics (China) 28, no. 4-6 (November 2011): 632–42. http://dx.doi.org/10.1007/s11767-012-0616-2.

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11

EL KHARBOUTLY, REHAB A., SWAPNA S. GOKHALE, and REDA A. AMMAR. "ARCHITECTURE-BASED SOFTWARE RELIABILITY ANALYSIS INCORPORATING CONCURRENCY." International Journal of Reliability, Quality and Safety Engineering 14, no. 05 (October 2007): 479–99. http://dx.doi.org/10.1142/s0218539307002751.

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With the growing complexity of software applications and increasing reliance on the services provided by these applications, architecture-based reliability analysis has become the focus of several recent research efforts. Most of the prevalent research in this area does not consider simultaneous or concurrent execution of application components. Concurrency, however, may be common in modern software applications. Thus, reliability analysis considering concurrent component execution within the context of the application architecture is necessary for contemporary software applications. This paper presents an architecture-based reliability analysis methodology for concurrent software applications. Central to the methodology is a state space approach, based on discrete time Markov chains (DTMCs), to represent the application architecture taking into consideration simultaneous component execution. A closed form, analytical expression for the expected application reliability based on the average execution times, constant failure rates, and the average number of visits to the components is derived. The average number of visits to application components are obtained from the solution of the DTMC model representing the application architecture. The potential of the methodology to facilitate sensitivity analysis, identification of reliability bottlenecks, and an assessment of the impact of workload and component changes, in addition to providing a reliability estimate, is discussed. To enable the application of the methodology in practice, estimation of model parameters from different software artifacts is described. The methodology is illustrated with a case study. Finally, strategies to alleviate the state space explosion issue for an efficient application of the methodology are proposed.

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12

Malik, Preeti, Lata Nautiyal, and Mangey Ram. "A Method for Considering Error Propagation in Reliability Estimation of Component-Based Software Systems." International Journal of Mathematical, Engineering and Management Sciences 4, no. 3 (June 1, 2019): 635–53. http://dx.doi.org/10.33889//ijmems.2019.4.3-051.

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Component-based software engineering has proved itself as a strong pillar in software engineering community. Large number of factors are involved in the success of software product developed using Component-based software engineering, for instance, security, reliability, quality, safety, and testability. As the component-based software is made up of large number of components put together, therefore components’ reliability influence the reliability of the complete software. Numerous models and principles have been established to estimate the reliability of software by applying information regarding architecture, quality and security attributes of the software. Many researchers overlooked a major factor while estimating reliability of component-based software and that is error-propagation. Although it can be a case that the components are not dependent on each other and they are supplemented with the wrappers. However it is not true for many component-based applications. In this paper, a framework for reliability estimation has been proposed. In our proposal we have considered error propagation. We have analyzed the program structure and also presented how they are handled in estimation process. Further sensitivity analysis is done to identify the most sensitive component of the system. A numerical simulation is also presented for better understanding of the proposed framework.

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13

ORMANDJIEVA, OLGA, MANAR ABU TALIB, and ALAIN ABRAN. "RELIABILITY MODEL FOR COMPONENT-BASED SYSTEMS IN COSMIC (A CASE STUDY)." International Journal of Software Engineering and Knowledge Engineering 18, no. 04 (June 2008): 515–39. http://dx.doi.org/10.1142/s0218194008003763.

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Software component technology has a substantial impact on modern IT evolution. The benefits of this technology, such as reusability, complexity management, time and effort reduction, and increased productivity, have been key drivers of its adoption by industry. One of the main issues in building component-based systems is the reliability of the composed functionality of the assembled components. This paper proposes a reliability assessment model based on the architectural configuration of a component-based system and the reliability of the individual components, which is usage- or testing-independent. The goal of this research is to improve the reliability assessment process for large software component-based systems over time, and to compare alternative component-based system design solutions prior to implementation. The novelty of the proposed reliability assessment model lies in the evaluation of the component reliability from its behavior specifications, and of the system reliability from its topology; the reliability assessment is performed in the context of the implementation-independent ISO/IEC 19761:2003 International Standard on the COSMIC method chosen to provide the component's behavior specifications. In essence, each component of the system is modeled by a discrete time Markov chain behavior based on its behavior specifications with extended-state machines. Then, a probabilistic analysis by means of Markov chains is performed to analyze any uncertainty in the component's behavior. Our hypothesis states that the less uncertainty there is in the component's behavior, the greater the reliability of the component. The system reliability assessment is derived from a typical component-based system architecture with composite reliability structures, which may include the composition of the serial reliability structures, the parallel reliability structures and the p-out-of-n reliability structures. The approach of assessing component-based system reliability in the COSMIC context is illustrated with the railroad crossing case study.

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14

Wang, Jun, Wei Ru Chen, De Lin Jing, and Hong Ji Yang. "Contribution Analysis of the Component in the Process of Reliability-Oriented Software Evolution." Advanced Materials Research 187 (February 2011): 78–83. http://dx.doi.org/10.4028/www.scientific.net/amr.187.78.

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According to the various components will play a different role in the system's reliability evolution, software architecture reliability is analyzed by architecture-based software reliability model in this paper. At the same time, the method of contribution analysis of the component in the process of reliability-oriented software evolution is proposed. The method shows that which component of the overall system plays an important or crucial role in the process of reliability evolution. The efficiency of the software evolution can be improved through contribution analysis of the component in the process of reliability-oriented software evolution.

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15

Chinnaiyan, R., and S. Somasundaram. "Evaluating the reliability of component‐based software systems." International Journal of Quality & Reliability Management 27, no. 1 (January 12, 2010): 78–88. http://dx.doi.org/10.1108/02656711011009326.

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16

Bagheri, Ebrahim, and Faezeh Ensan. "Reliability estimation for component-based software product lines." Canadian Journal of Electrical and Computer Engineering 37, no. 2 (2014): 94–112. http://dx.doi.org/10.1109/cjece.2014.2323958.

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17

Fiondella, Lance, Sanguthevar Rajasekaran, and Swapna S. Gokhale. "Efficient Software Reliability Analysis With Correlated Component Failures." IEEE Transactions on Reliability 62, no. 1 (March 2013): 244–55. http://dx.doi.org/10.1109/tr.2013.2241131.

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18

Vijayalakshmi, K. "Reliability improvement in component-based software development environment." International Journal of Information Systems and Change Management 5, no. 2 (2011): 99. http://dx.doi.org/10.1504/ijiscm.2011.041510.

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19

Loganathan, A., and Muthuraj R Jeromia. "Naive Principal Component Analysis in Software Reliability Studies." International Journal of Computational and Theoretical Statistics 06, no. 01 (May 1, 2019): 28–32. http://dx.doi.org/10.12785/ijcts/060104.

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20

Prasad, Gopal, and Ram Nivas. "Software Reliability Estimation of Component based Software System using Fuzzy Logic." International Journal of Computer Applications 127, no. 7 (October 15, 2015): 16–20. http://dx.doi.org/10.5120/ijca2015904871.

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21

TAMURA, YOSHINOBU, and SHIGERU YAMADA. "A COMPONENT-ORIENTED RELIABILITY ASSESSMENT METHOD FOR OPEN SOURCE SOFTWARE." International Journal of Reliability, Quality and Safety Engineering 15, no. 01 (February 2008): 33–53. http://dx.doi.org/10.1142/s0218539308002915.

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Software development environment has been changing into new development paradigms such as concurrent distributed development environment and the so-called open source project by using network computing technologies. Especially, an OSS (open source software) system which serves as key components of critical infrastructures in the society is still ever-expanding now. In case of considering the effect of the debugging process on an entire system in the development of a method of reliability assessment for the OSS, it is necessary to grasp the deeply-intertwined factors, such as programming path, size of each component, skill of fault reporter, and so on. In order to consider the effect of each software component on the reliability of an entire system, we propose a new approach to user-oriented software reliability assessment by creating a fusion of neural network and software reliability growth modeling. In this paper, we show application examples of component-oriented software reliability assessment based on neural network and software reliability growth modeling for the OSS. Also, we analyze actual software fault count data to show numerical examples of software reliability assessment for the OSS. Moreover, we develop the testing management tool for OSS.

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22

Li, Xiao-Dan, Yong-Feng Yin, and Lance Fiondella. "Reliability and Performance Analysis of Architecture-Based Software Implementing Restarts and Retries Subject to Correlated Component Failures." International Journal of Software Engineering and Knowledge Engineering 25, no. 08 (October 2015): 1307–34. http://dx.doi.org/10.1142/s0218194015500266.

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High reliability and performance are essential attributes of software systems designed for critical real-time applications. To improve the reliability and performance of software, many systems incorporate some form of fault recovery mechanism. However, contemporary models of software reliability and performance rarely consider these fault recovery mechanisms. Another notable shortcoming of many software models is that they make the simplifying assumption that component failures are statistically independent, which disagrees with several experimental studies that have shown that the failures of software components can exhibit correlation. This paper presents an architecture-based model of software reliability and performance that explicitly considers a two-stage fault recovery mechanism implementing component restarts and application-level retries. The application architecture is characterized by a Discrete Time Markov Chain (DTMC) to represent the dynamic branching behavior of control between the components of the application. Correlations between the component failures are computed with an efficient numerical algorithm for a multivariate Bernoulli (MVB) distribution. We illustrate the utility of the model through a case study of an embedded software application. The results suggest that the model can be used to quantify the impact of software fault recovery and correlated component failures on application reliability and performance.

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23

Kaur, Amandeep, Puninder Kaur, and Payal Kaushal. "Maintainability Procedure in Component-Based Software." Journal of Computational and Theoretical Nanoscience 17, no. 11 (November 1, 2020): 5156–61. http://dx.doi.org/10.1166/jctn.2020.9357.

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Changes in service requirement of software demands consequent changes in its maintainability. An important aspect of changes is that it affects various factors in Component Based Software Engineering which is reuse-based approach to define, implement, and integrate different components into system. Variety of Component-based software frameworks for distributed, real-time and embedded systems in Component-oriented programming are existing for specific domains in order to deal with different requirements. Functionalities under component based system affecting multiple factors in a distributed environment. It is therefore more than necessary to consider various quality attributes like reliability, maintainability, interpretability and reusability for determining quality assurance. The article presents an approach to enhance the promptness of system maintainability in case of changes in component based software.

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24

XUE, Lixing, Decheng ZUO, Zhan ZHANG, and Na WU. "A Novel Component Ranking Method for Improving Software Reliability." IEICE Transactions on Information and Systems E100.D, no. 10 (2017): 2653–58. http://dx.doi.org/10.1587/transinf.2017edl8043.

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25

Su, Xihong, Hongwei Liu, Zhibo Wu, Xiaozong Yang, and Decheng Zuo. "SA based software deployment reliability estimation considering component dependence." Journal of Electronics (China) 28, no. 1 (January 2011): 118–25. http://dx.doi.org/10.1007/s11767-011-0561-5.

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26

Chopra, Sandeep, Lata Nautiyal, Preeti Malik, Mangey Ram, and Mahesh K. Sharma. "A Non-Parametric Approach for Survival Analysis of Component-Based Software." International Journal of Mathematical, Engineering and Management Sciences 5, no. 2 (April 1, 2020): 309–18. http://dx.doi.org/10.33889/ijmems.2020.5.2.025.

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Reliability of a software or system is the probability of system to perform its functions adequately for the stated time period under specific environment conditions. In case of component-based software development reliability estimation is a crucial factor. Existing reliability estimation model falls into two broad categories parametric and non-parametric models. Parametric models approximate the model parameters based on the assumptions of fundamental distributions. Non-parametric models enable parameter estimation of the software reliability growth models without any assumptions. We have proposed a novel non-parametric approach for survival analysis of components. Failure data is collected based on which we have calculated failure rate and reliability of the software. Failure rate increases with the time whereas reliability decreases with the time.

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27

Li, Zhen, Junfeng Tian, and Pengyuan Zhao. "Software Reliability Estimate with Duplicated Components Based on Connection Structure." Cybernetics and Information Technologies 14, no. 3 (September 1, 2014): 3–13. http://dx.doi.org/10.2478/cait-2014-0028.

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Abstract Reliability testing of complex software at the system level is impossible due to the environmental constraint or the time limitation, so its reliability estimate is often obtained based on the reliability of subsystems or components. The connection structure was defined and the component-based software reliability was estimated based on it. For the present popular software with duplicated components, an approach to variance estimation of software reliability for complex structure systems was proposed, which has improved the hierarchical decomposition approach of variance estimation just for series-parallel systems. Experimental results indicated that the approach to variance estimation for reliability of software with duplicated components has advantages, such as the simple calculation process, small error result, and suitability for complex structure systems. Finally, the sensitivity analysis, used to identify critical components for resource allocation, could better improve the software reliability

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28

Orso, Alessandro, Hyunsook Do, Gregg Rothermel, Mary Jean Harrold, and David S. Rosenblum. "Using component metadata to regression test component-based software." Software Testing, Verification and Reliability 17, no. 2 (2007): 61–94. http://dx.doi.org/10.1002/stvr.344.

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29

Zhao, Li Min. "MASTA Software for Wind Turbine Gearbox Design." Advanced Materials Research 490-495 (March 2012): 2259–63. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.2259.

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Wind turbine technology has developed rapidly in recent years. Increasing component reliability to achieve 20% wind energy will require advancements in component design, materials, and testing methods to validate wind-turbine components in the harshest environments. At the core of many of the solutions offered by SMT is MASTA, our fully integrated software tool for the complete design, analysis and optimization of transmission systems. Designers & engineers can use MASTA to rapidly predict key transmission performance characteristics and to identify any design faults or weaknesses before any manufacturing is started, saving both time and money. MASTA’s reliable analysis techniques can improve the power, performance, reliability, and cost effectiveness of wind turbines. The mix of commercial and proprietary software will provide a complete assessment and optimization of the wind turbine through detailed dynamic and static analysis of key component functions for their efficiency and design life.

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30

Gao, Zhi Jie, Fu Chun Sun, and Ling Lu. "Software Reliability Test Methods Based on Component Composition and Markov Process." Advanced Materials Research 660 (February 2013): 169–73. http://dx.doi.org/10.4028/www.scientific.net/amr.660.169.

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With the continuous improvement of the software quality requirements, more and more attentions are paid to the reliability of software systems. Most of the existing reliability models rely on the cumulative test time to characterize the reliability improvement, while ignoring the saturation effects and software module failure characteristic differences exist, making the test results to low confidence. In this paper, Markov description methods are utilized in reliability calculating process to improve the fictitious-excellence problem in current testing models. Finally, the method is applied to a flight control system software reliability testing. The results demonstrate the effectiveness of the method.

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31

HOU, Chunyan, Chen CHEN, Jinsong WANG, and Kai SHI. "A Scenario-Based Reliability Analysis Approach for Component-Based Software." IEICE Transactions on Information and Systems E98.D, no. 3 (2015): 617–26. http://dx.doi.org/10.1587/transinf.2014edp7241.

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32

R, Chinnaiyan, and S. Somasundaram. "Neural Networks Based Approach for Component Based Software Reliability Evaluation." i-manager's Journal on Software Engineering 3, no. 3 (March 15, 2009): 6–10. http://dx.doi.org/10.26634/jse.3.3.188.

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33

Tyagi, Kirti, and Arun Sharma. "Significant Factors for Reliability Estimation of Component Based Software Systems." Journal of Software Engineering and Applications 07, no. 11 (2014): 934–42. http://dx.doi.org/10.4236/jsea.2014.711083.

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34

Palviainen, Marko, Antti Evesti, and Eila Ovaska. "The reliability estimation, prediction and measuring of component-based software." Journal of Systems and Software 84, no. 6 (June 2011): 1054–70. http://dx.doi.org/10.1016/j.jss.2011.01.048.

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35

Yacoub, S., B. Cukic, and H. H. Ammar. "A Scenario-Based Reliability Analysis Approach for Component-Based Software." IEEE Transactions on Reliability 53, no. 4 (December 2004): 465–80. http://dx.doi.org/10.1109/tr.2004.838034.

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36

WenQinq, Li, Xu Chenguan, Rao Yao, Zhu Liangliang, and Yi Yongxian. "Research on system software reliability based on service component architecture." Journal of Physics: Conference Series 2005, no. 1 (August 1, 2021): 012022. http://dx.doi.org/10.1088/1742-6596/2005/1/012022.

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37

MohanRoopa Y and Ramamohan Reddy A. "An Optimized Component Selection Algorithm for Self-Adaptive Software Architecture Using the Component Repository." International Journal of Software Science and Computational Intelligence 11, no. 2 (April 2019): 51–61. http://dx.doi.org/10.4018/ijssci.2019040104.

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Component-based software engineering focuses on the development and reuse of components. The component reuse depends on the storage and retrieval processes. This article presents the component repository model for the developers to achieve good productivity. The component selection from the component repository according to the functionality and requirements is a crucial process. This article proposed an algorithm for optimizing component selection with functionality constraints like customer size, reliability, and performance. The experimental result evaluates the performance of the algorithm.

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38

Sharma, Ravi Kumar, and Dr Parul Gandhi. "Estimate reliability of component-based software sys-tem using modified neuro fuzzy model." International Journal of Engineering & Technology 6, no. 2 (May 24, 2017): 45. http://dx.doi.org/10.14419/ijet.v6i2.7722.

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There are many algorithms and techniques for estimating the reliability of Component Based Software Systems (CBSSs). Accurate esti-mation depends on two factors: component reliability and glue code reliability. Still much more research is expected to estimate reliability in a better way. A number of soft computing approaches for estimating CBSS reliability has been proposed. These techniques learnt from the past and capture existing patterns in data. In this paper, we proposed new model for estimating CBSS reliability known as Modified Neuro Fuzzy Inference System (MNFIS). This model is based on four factors Reusability, Operational, Component dependency, Fault Density. We analyze the proposed model for diffent data sets and also compare its performance with that of plain Fuzzy Inference System. Our experimental results show that, the proposed model gives better reliability as compare to FIS.

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39

XUE, Lixing, Zhan ZHANG, and Decheng ZUO. "Identifying Significant Components of Complex Software for Improving Reliability: Using Invocation Relationships and Component Characteristics." Journal of Physics: Conference Series 1284 (August 2019): 012041. http://dx.doi.org/10.1088/1742-6596/1284/1/012041.

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40

HOU, Chunyan, Jinsong WANG, and Chen CHEN. "Using Hierarchical Scenarios to Predict the Reliability of Component-Based Software." IEICE Transactions on Information and Systems E101.D, no. 2 (2018): 405–14. http://dx.doi.org/10.1587/transinf.2017edp7127.

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41

Chinnaiyan, R., and S. Somasundaram. "MONTE Carlo Simulation for Reliability Assessment of Component Based Software Systems." i-manager's Journal on Software Engineering 4, no. 4 (June 15, 2010): 27–32. http://dx.doi.org/10.26634/jse.4.4.1171.

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42

Yadav, Shivani. "Component-Based Software System using Computational Intelligence Technique for Reliability Prediction." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 3 (June 25, 2020): 3708–21. http://dx.doi.org/10.30534/ijatcse/2020/184932020.

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43

Kaliraj, S., and A. Bharathi. "Path testing based reliability analysis framework of component based software system." Measurement 144 (October 2019): 20–32. http://dx.doi.org/10.1016/j.measurement.2018.11.086.

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44

Chen, L., W. U. Kaigui, and H. E. Pan. "Dynamic Software Reliability Maintenance Based on Component Monitoring and Resource Allocation." Information Technology Journal 10, no. 11 (October 15, 2011): 2214–19. http://dx.doi.org/10.3923/itj.2011.2214.2219.

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45

Li, Xiaodan, Yongfeng Yin, Lance Fiondella, and Yibin Zhou. "Software reliability analysis considering correlated component failures with coupling measurement framework." Journal of Systems Engineering and Electronics 26, no. 5 (October 2015): 1114–26. http://dx.doi.org/10.1109/jsee.2015.00121.

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46

KRISTIANSEN, MONICA, RUNE WINTHER, and BENT NATVIG. "A BAYESIAN HYPOTHESIS TESTING APPROACH FOR FINDING UPPER BOUNDS FOR PROBABILITIES THAT PAIRS OF SOFTWARE COMPONENTS FAIL SIMULTANEOUSLY." International Journal of Reliability, Quality and Safety Engineering 18, no. 03 (June 2011): 209–36. http://dx.doi.org/10.1142/s021853931100410x.

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Predicting the reliability of software systems based on a component-based approach is inherently difficult, in particular due to failure dependencies between software components. One possible way to assess and include dependency aspects in software reliability models is to find upper bounds for probabilities that software components fail simultaneously and then include these into the reliability models. In earlier research, it has been shown that including partial dependency information may give substantial improvements in predicting the reliability of compound software compared to assuming independence between all software components. Furthermore, it has been shown that including dependencies between pairs of data-parallel components may give predictions close to the system's true reliability. In this paper, a Bayesian hypothesis testing approach for finding upper bounds for probabilities that pairs of software components fail simultaneously is described. This approach consists of two main steps: (1) establishing prior probability distributions for probabilities that pairs of software components fail simultaneously and (2) updating these prior probability distributions by performing statistical testing. In this paper, the focus is on the first step in the Bayesian hypothesis testing approach, and two possible procedures for establishing a prior probability distribution for the probability that a pair of software components fails simultaneously are proposed.

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47

El Kharboutly, Rehab, and Swapna S. Gokhale. "Efficient Reliability Analysis of Concurrent Software Applications Considering Software Architecture." International Journal of Software Engineering and Knowledge Engineering 24, no. 01 (February 2014): 43–60. http://dx.doi.org/10.1142/s0218194014500028.

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Architecture-based reliability analysis of software applications is gaining prominence as it can provide valuable guidance to software architects during early design phases. Concurrent component execution is common among modern software applications, and hence, reliability analysis considering concurrency within the context of software architecture is essential. Our preliminary analysis approach considering concurrency suffers from state-space explosion; due to which it cannot be applied to practical software applications. This paper proposes solutions to the model specification and solution challenges arising from the state-space explosion problem. The specification challenge is alleviated using the Stochastic Reward Net (SRN) modelling paradigm which can intuitively and concisely represent concurrent software architecture at a higher level of abstraction. The computational challenge is alleviated by aggregating the original state space. Our illustrations show that aggregation introduces only a small inaccuracy into the reliability estimate, and hence, preserves the utility of the original model to produce design guidance via sensitivity analysis.

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48

Zhang, Qifan, Liqiong Xu, Weihua Yang, and Shanshan Yin. "Reliability Analysis of the Generalized Exchanged Hypercube." Parallel Processing Letters 30, no. 02 (June 2020): 2050009. http://dx.doi.org/10.1142/s0129626420500097.

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Let [Formula: see text] be a non-complete graph, a subset [Formula: see text] is called a [Formula: see text]-component cut of [Formula: see text], if [Formula: see text] is disconnected and has at least [Formula: see text] components. The cardinality of the minimum [Formula: see text]-component cut is the [Formula: see text]-component connectivity of [Formula: see text] and is denoted by [Formula: see text]. The [Formula: see text]-component connectivity is a natural extension of the classical connectivity. As an application, the [Formula: see text]-component connectivity can be used to evaluate the reliability and fault tolerance of an interconnection network structure based on a graph model. In a previous work, E. Cheng et al. obtained the [Formula: see text]-component connectivity of the generalized exchanged hypercube [Formula: see text] for [Formula: see text] and [Formula: see text]. In this paper, we continue the work and determine that [Formula: see text] for [Formula: see text]. Moreover, we show that every optimal [Formula: see text]-component cut of [Formula: see text] is trivial for [Formula: see text] and [Formula: see text].

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49

Shekhar, Chandra, Neeraj Kumar, Madhu Jain, and Amit Gupta. "Reliability Prediction of Computing Network with Software and Hardware Failures." International Journal of Reliability, Quality and Safety Engineering 27, no. 02 (September 9, 2019): 2040006. http://dx.doi.org/10.1142/s0218539320400069.

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In this paper, we investigate the reliability and queueing performance indices for the fault-tolerant computing network having a finite number of unreliable operating components with the provision of warm standby components. Operating and standby components are governed by dedicated software which is also prone to random failure. On failure of operating components, available standby component(s) may switch from the standby state to operating state with negligible switchover time. The switchover process may also fail due to some automation hindrance. The computing network is also subjected to common cause failure in lieu of external cause. The studied redundant fault-tolerant computing network is framed as a Markovian machine interference model with exponentially distributed inter-failure times and service times. For the reliability prediction of the computing network, various performance measures, namely, mean-time-to-failure (MTTF), reliability/availability, failure frequency, etc., have been formulated in terms of transient-state probabilities which we have obtained using the spectral method. To show the practicability of the developed model, numerical simulation has been done. Sensitivity analysis of reliability and other indices of the computing network with respect to different network parameters has been presented, and results are summarized in the tables and graphs. Finally, future scope and concluding remarks have been included.

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Senthil, R., D. S. Kushwaha, and A. K. Misra. "An Extended Component Model and its evaluation for Reliability & Quality." Journal of Object Technology 7, no. 7 (2008): 109. http://dx.doi.org/10.5381/jot.2008.7.7.a3.

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