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Journal articles on the topic 'JELINSKI-MORANDA MODEL'

Author: Grafiati
Published: 11 September 2023
Last updated: 31 July 2025

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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 (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 (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 (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 (2013): 1350009. http://dx.doi.org/10.1142/s0218539313500095.

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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 Jelins
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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 (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 (2012): 467–81. http://dx.doi.org/10.1080/00207160.2011.642299.

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8

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

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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
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9

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

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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 (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 (2013): 295–306. http://dx.doi.org/10.2478/auom-2013-0060.

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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
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12

Sangeeta, Sitender, Rachna Jain, and Ankita Bansal. "Bug Report Analytics for Software Reliability Assessment using Hybrid Swarm – Evolutionary Algorithm." e-Informatica Software Engineering Journal 19, no. 1 (2025): 250101. http://dx.doi.org/10.37190/e-inf250101.

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Background: With the growing advances in the digital world, software development demands are increasing at an exponential rate. To ensure reliability of the software, high-performance tools for bug report analysis are needed. Aim: This paper proposes a new ‘Iterative Software Reliability’ model based on one of the most recent Software Development Life Cycle (SDLC) approach. Method: The proposed iterative failure rate model assumes that new functionality enhancement occurs in each iteration of software development and accordingly design modification is made at each stage of software development
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13

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

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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, re
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14

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

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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-sh
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15

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 (2014): 1450031. http://dx.doi.org/10.1142/s0218539314500314.

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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 mo
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16

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

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17

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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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.
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18

Natarajan, Nageswari, Ansuman Mahapatra, and Ghanshaym S. Mahapatra. "Imperfect debugging-based modelling of fault detection incorporating change point for software reliability analysis." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, November 28, 2024. http://dx.doi.org/10.1177/1748006x241293501.

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The fault detection rate during the operational phase is directly proportional to the testing and debugging efforts during the software development. Extensive testing on software often reveals hidden faults previously undetected. During fault detection, the change points identify significant variations in software reliability, indicating possible faults. These helps test managers and engineers assess testing effectiveness, evaluate the impact of modifications and accurately track testing progress. Imperfect-debugging models can predict software reliability based on how often faults are found,
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19

Куликовская, А. А., Е. А. Доренская та Ю. А. Семенов. "Количественные характеристики безопасности программ". Международный научный журнал "Современные информационные технологии и ИТ-образование" 18, № 4 (2022). https://doi.org/10.25559/sitito.18.202204.855-860.

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Качество программ принято характеризовать числом ошибок на 1000 строк кода. Эта характеристика получается в результате регрессионного анализа числа выявленных ошибок в последовательных версиях кода с последующей экстраполяцией в отдаленное будущее. Данная процедура является очень трудоемкой даже для крупных компаний. Проверить достоверность такой оценки для обычных пользователей как правило довольно сложно. Такая проблема возникает из-за недоступности исходных данных. Существуют различные способы оценки числа ошибок в программе, например, модель Шумана, Муса, Ла Падула, Джелинского-Моранды, Ши
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20

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

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