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Автор: Grafiati
Опубліковано: 11 вересня 2023

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1

S.Mahapatra, G., and P. Roy. "Modified Jelinski-Moranda Software Reliability Model with Imperfect Debugging Phenomenon." International Journal of Computer Applications 48, no. 18 (June 30, 2012): 38–46. http://dx.doi.org/10.5120/7451-0534.

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2

Al Turk, Lutfiah Ismail, and Eftekhar Gabel Alsolami. "Jelinski-Moranda Software Reliablity Growth Model : A Brief Literature and Modification." International Journal of Software Engineering & Applications 7, no. 2 (March 31, 2016): 33–44. http://dx.doi.org/10.5121/ijsea.2016.7204.

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3

Littlewood, Bev, and Ariela Sofer. "A Bayesian modification to the Jelinski-Moranda software reliability growth model." Software Engineering Journal 2, no. 2 (1987): 30. http://dx.doi.org/10.1049/sej.1987.0005.

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4

Washburn, Alan. "A sequential Bayesian generalization of the Jelinski–Moranda software reliability model." Naval Research Logistics 53, no. 4 (June 2006): 354–62. http://dx.doi.org/10.1002/nav.20148.

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5

INOUE, SHINJI, SHIHO HAYASHIDA, and SHIGERU YAMADA. "EXTENDED HAZARD RATE MODELS FOR SOFTWARE RELIABILITY ASSESSMENT WITH EFFECT AT CHANGE-POINT." International Journal of Reliability, Quality and Safety Engineering 20, no. 02 (April 2013): 1350009. http://dx.doi.org/10.1142/s0218539313500095.

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Анотація:
A software hazard rate model is known as one of the important and useful mathematical models for describing the software failure occurrence phenomenon observed in a testing phase. It is difficult to say that the testing environment always constant during a testing phase due to changing the specification and fault target and so forth. Therefore, taking into consideration of the effect of the change in software reliability growth modeling is expected to conduct more accurate software reliability assessment. In this paper, we develop extended software hazard rate models based on well-known Jelinski–Moranda and Moranda models, by considering with a change of testing environment. Especially in this paper, we incorporate the uncertainty of the effect of the change on the software reliability growth process into the software hazard rate modeling. Finally, we show numerical examples for our models and results of model comparisons by using actual data.
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6

Barghout, May. "Predicting software reliability using an imperfect debugging Jelinski Moranda Non-homogeneous Poisson Process model." Model Assisted Statistics and Applications 5, no. 1 (February 11, 2010): 31–41. http://dx.doi.org/10.3233/mas-2010-0127.

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7

Jukić, Dragan. "TheLp-norm estimation of the parameters for the Jelinski–Moranda model in software reliability." International Journal of Computer Mathematics 89, no. 4 (March 2012): 467–81. http://dx.doi.org/10.1080/00207160.2011.642299.

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8

Van Pul, Mark. "Simulations on the Jelinski-Moranda model of software reliability; application of some parametric bootstrap methods." Statistics and Computing 2, no. 3 (September 1992): 121–36. http://dx.doi.org/10.1007/bf01891204.

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9

boland, Philip J., Frank proschan, and Y. L. Tong. "Fault Diversity in Software Reliability." Probability in the Engineering and Informational Sciences 1, no. 2 (April 1987): 175–87. http://dx.doi.org/10.1017/s0269964800000383.

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Анотація:
Diversity of bugs or faults in a software system is a factor contributing to software unreliability which has not yet been appropriately emphasized. This paper is written with the intention of demonstrating the impact of fault diversity on the time to detection of software bugs. A new discrete software reliability model based on the multinomial distribution is introduced. It is shown that for models of this type, the more diverse the fault probabilities are, the longer (stochastically) it takes to detect or eliminate any n faults, while the smaller (stochastically) will be the number of faults detected or eliminated during a given amount of time (or during a given number of inputs to the system). The impact of fault diversity is also demonstrated for the Jelinski–Moranda model.
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10

Al turk, Lutfiah Ismail, and Eftekhar Gabel Alsolami. "A Comparison Study of Estimation Methods for Generalized Jelinski-Moranda Model Based on Various Simulated Patterns." International Journal of Software Engineering & Applications 7, no. 3 (May 30, 2016): 27–48. http://dx.doi.org/10.5121/ijsea.2016.7303.

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11

Velcescu, Letitia. "Distribution of Time Interval between the Modifications of Result Sets Cardinalities in Random Databases." Analele Universitatii "Ovidius" Constanta - Seria Matematica 21, no. 3 (November 1, 2013): 295–306. http://dx.doi.org/10.2478/auom-2013-0060.

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Анотація:
AbstractIn this paper, we propose a method to estimate the probability distribution of the time interval which ellapses between the modifications of the cardinality in a random database query’s result set. This type of database is important either in modeling uncertainty or storing data whose values follow a probability distribution. The result that we introduce is important from the point of view of the database optimization, providing a useful method for an integrated module. In previous research on random databases the sizes of some relational operations results were investigated. This kind of information is rather useful in an analytical database which provides decision-making support. The result we particularly aim to present in this paper concerns the transactional random databases, addressing its specific functionality. It will be proven that the interval of time between the cardinalities changes is exponentially distributed. The proof is based on the technique of the Markovian Jelinski-Moranda model, which is used in the reliability of software programs.
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12

van Driel, Willem Dirk, Jan Willem Bikker, Matthijs Tijink, and Alessandro Di Bucchianico. "Software Reliability for Agile Testing." Mathematics 8, no. 5 (May 13, 2020): 791. http://dx.doi.org/10.3390/math8050791.

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Анотація:
It is known that quantitative measures for the reliability of software systems can be derived from software reliability models, and, as such, support the product development process. Over the past four decades, research activities in this area have been performed. As a result, many software reliability models have been proposed. It was shown that, once these models reach a certain level of convergence, it can enable the developer to release the software and stop software testing accordingly. Criteria to determine the optimal testing time include the number of remaining errors, failure rate, reliability requirements, or total system cost. In this paper, we present our results in predicting the reliability of software for agile testing environments. We seek to model this way of working by extending the Jelinski–Moranda model to a “stack” of feature-specific models, assuming that the bugs are labeled with the features they belong to. In order to demonstrate the extended model, two use cases are presented. The questions to be answered in these two cases are: how many software bugs remain in the software and should one decide to stop testing the software?
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13

Vijayalakshmi, G. "Dependability Analysis of Homogeneous Distributed Software/Hardware Systems." International Journal of Reliability, Quality and Safety Engineering 22, no. 02 (April 2015): 1550007. http://dx.doi.org/10.1142/s0218539315500072.

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Анотація:
With the increasing demand for high availability in safety-critical systems such as banking systems, military systems, nuclear systems, aircraft systems to mention a few, reliability analysis of distributed software/hardware systems continue to be the focus of most researchers. The reliability analysis of a homogeneous distributed software/hardware system (HDSHS) with k-out-of-n : G configuration and no load-sharing nodes is analyzed. However, in practice the system load is shared among the working nodes in a distributed system. In this paper, the dependability analysis of a HDSHS with load-sharing nodes is presented. This distributed system has a load-sharing k-out-of-(n + m) : G configuration. A Markov model for HDSHS is developed. The failure time distribution of the hardware is represented by the accelerated failure time model. The software faults are detected during software testing and removed upon failure. The Jelinski–Moranda software reliability model is used. The maintenance personal can repair the system up on both software and hardware failure. The dependability measures such as reliability, availability and mean time to failure are obtained. The effect of load-sharing hosts on system hazard function and system reliability is presented. Furthermore, an availability comparison of our results and the results in the literature is presented.
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14

DAMODARAN, D., B. RAVIKUMAR, and VELIMUTHU RAMACHANDRAN. "BAYESIAN SOFTWARE RELIABILITY MODEL COMBINING TWO PRIORS AND PREDICTING TOTAL NUMBER OF FAILURES AND FAILURE TIME." International Journal of Reliability, Quality and Safety Engineering 21, no. 06 (December 2014): 1450031. http://dx.doi.org/10.1142/s0218539314500314.

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Анотація:
Reliability statistics is divided into two mutually exclusive camps and they are Bayesian and Classical. The classical statistician believes that all distribution parameters are fixed values whereas Bayesians believe that parameters are random variables and have a distribution of their own. Bayesian approach has been applied for the Software Failure data and as a result of that several Bayesian Software Reliability Models have been formulated for the last three decades. A Bayesian approach to software reliability measurement was taken by Littlewood and Verrall [A Bayesian reliability growth model for computer software, Appl. Stat. 22 (1973) 332–346] and they modeled hazard rate as a random variable. In this paper, a new Bayesian software reliability model is proposed by combining two prior distributions for predicting the total number of failures and the next failure time of the software. The popular and realistic Jelinski and Moranda (J&M) model is taken as a base for bringing out this model by applying Bayesian approach. It is assumed that one of the parameters of JM model N, number of faults in the software follows uniform prior distribution and another failure rate parameter Φi follows gama prior distribution. The joint prior p(N, Φi) is obtained by combining the above two prior distributions. In this Bayesian model, the time between failures follow exponential distribution with failure rate parameter with stochastically decreasing order on successive failure time intervals. The reasoning for the assumption on the parameter is that the intention of the software tester to improve the software quality by the correction of each failure. With Bayesian approach, the predictive distribution has been arrived at by combining exponential Time between Failures (TBFs) and joint prior p(N, Φi). For the parameter estimation, maximum likelihood estimation (MLE) method has been adopted. The proposed Bayesian software reliability model has been applied to two sets of act. The proposed model has been applied to two sets of actual software failure data and it has been observed that the predicted failure times as per the proposed model are closer to the actual failure times. The predicted failure times based on Littlewood–Verall (LV) model is also computed. Sum of square errors (SSE) criteria has been used for comparing the actual time between failures and predicted time between failures based on proposed model and LV model.
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15

Joe, H., and N. Reid. "On the Software Reliability Models of Jelinski-Moranda and Littlewood." IEEE Transactions on Reliability R-34, no. 3 (August 1985): 216–18. http://dx.doi.org/10.1109/tr.1985.5222120.

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16

Haque, Md Asraful, and Nesar Ahmad. "Key Issues in Software Reliability Growth Models." Recent Advances in Computer Science and Communications 13 (October 12, 2020). http://dx.doi.org/10.2174/2666255813999201012182821.

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Анотація:
Background: Software Reliability Growth Models (SRGMs) are most widely used mathematical models to monitor, predict and assess the software reliability. They play an important role in industries to estimate the release time of a software product. Since 1970s, researchers have suggested a large number of SRGMs to forecast software reliability based on certain assumptions. They all have explained how the system reliability changes over time by analyzing failure data set throughout the testing process. However, none of the models is universally accepted and can be used for all kinds of software. Objective: The objective of this paper is to highlight the limitations of SRGMs and to suggest a novel approach towards the improvement. Method: We have presented the mathematical basis, parameters and assumptions of software reliability model and analyzed five popular models namely Jelinski-Moranda (J-M) Model, Goel Okumoto NHPP Model, Musa-Okumoto Log Poisson Model, Gompertz Model and Enhanced NHPP Model. Conclusion: The paper focuses on the many challenges like flexibility issues, assumptions, and uncertainty factors of using SRGMs. It emphasizes considering all affecting factors in reliability calculation. A possible approach has been mentioned at the end of the paper.
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17

"On the software reliability models of Jelinski—Moranda and Littlewood." Microelectronics Reliability 26, no. 6 (January 1986): 1191. http://dx.doi.org/10.1016/0026-2714(86)90841-3.

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