Bayesian network based software reliability prediction with an operational profile

Bai, Chenggang

doi:10.1016/j.jss.2004.11.034

Cited by 55 publications

(18 citation statements)

References 35 publications

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“…So, when the goal is to improve process productivity and the quality of the resulting products (i.e., a software application), artificial intelligence becomes a key distinguishing feature [33,51]. In particular, the approach based on Bayesian networks is becoming increasingly popular within the software engineering community, as these networks are capable of providing more appropriate solutions to some of the problems encountered in this field, [7,25,27,59,72].…”

Section: Introductionmentioning

confidence: 99%

Bayesian networks for enhancement of requirements engineering: a literature review

Cano

2015

Requirements Eng

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Requirements analysis is the software engineering stage that is closest to the users' world. It also involves tasks that are knowledge intensive. Thus, the use of Bayesian networks (BNs) to model this knowledge would be a valuable aid. These probabilistic models could manage the imprecision and ambiguities usually present in requirements engineering (RE). In this work, we conduct a literature review focusing on where and how BNs are applied on subareas of RE in order to identify which gaps remain uncovered and which methods might engineers employ to incorporate this intelligent technique into their own requirements processes. The scarcity of identified studies (there are only 20) suggests that not all RE areas have been properly investigated in the literature. The evidence available for adopting BNs into RE is sufficiently mature yet the methods applied are not easily translatable to other topics. Nonetheless, there are enough studies supporting the applicability of synergistic cooperation between RE and BNs. This work provides a background for understanding the current state of research encompassing RE and BNs. Functional, non-functional and -ilities requirements artifacts are enhanced by the use of BNs. These models were obtained by interacting with experts or by learning from databases. The most common criticism from the point of view of BN experts is that the models lack validation, whereas requirements engineers point to the lack of a clear application method for BNs and the lack of tools for incorporating them as built-in help functions.

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Section: Introductionmentioning

confidence: 99%

Bayesian networks for enhancement of requirements engineering: a literature review

Cano

2015

Requirements Eng

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“…The relationships of them are: 10 A 3,10 = A 3,9 + t o 3,4 , · · · Mark the adjacent time segments of the time series above as an interval, which are denoted by π i,1 , π i,2 , · · · , π i,m (i = 1, 2, 3).…”

Section: Csp Dynamic Subsetmentioning

confidence: 99%

“…These Markov models are converted into differential equations and are solved numerically. Valid approaches that converting the Markov models into differential equations in DFT analysis are represented by Petri nets [6][7][8] , Bayesian networks [9][10][11][12] , and Monte Carlo simulation [13][14] . Amari and Dill [15] proposed a method to evaluate DFT by considering sequence failures.…”

Section: Introductionmentioning

confidence: 99%

Numerical simulation to reliability analysis of fault-tolerant repairable system

Liang

Hong

Zhang

et al. 2010

J. Shanghai Jiaotong Univ. (Sci.)

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In the traditional method for the reliability analysis of fault-tolerant system, the system structure is described by means of binary decision diagram (BDD) and Markov process, and then the reliability indexes are calculated. However, as the size of system augments, the size of state space will increase exponentially. Additionally, Markov approach requires that the failure and repair time of the components obey an exponential distribution. In this study, by combining dynamic fault tree (DFT) and numerical simulation based on the minimal sequence cut set (MSCS), a new method to evaluate reliability of fault-tolerant system with repairable components is proposed. The method presented does not depend on Markov model, so that it can effectively solve the problem of the state-space combination explosion. Moreover, it is suitable for systems whose failure and repair time obey an arbitrary distribution. Therefore, our method is more flexible than the traditional method. At last, an example is given to verify the method.

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“…Dynamic BNs (DBNs), which are longestablished extensions of ordinary BNs and allow explicit modeling of changes over time, were developed subsequently. In recent years, BNs and DBNs have been applied to study the reliability of multilevel systems [14], two-terminal networks [15], distributed communication systems [16], N-modular redundant systems [17], structural systems [18], human factors [19], and software systems [20]. BNs and DBNs have also been used to study the fault diagnosis of computer numerical control machine tools [21], chillers [22], and ground-source heat pumps [23].…”

Section: Introductionmentioning

confidence: 99%

Real-time reliability evaluation methodology based on dynamic Bayesian networks: A case study of a subsea pipe ram BOP system

Cai

Liu

et al. 2015

ISA Transactions

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Bayesian network based software reliability prediction with an operational profile

Cited by 55 publications

References 35 publications

Bayesian networks for enhancement of requirements engineering: a literature review

Bayesian networks for enhancement of requirements engineering: a literature review

Numerical simulation to reliability analysis of fault-tolerant repairable system

Real-time reliability evaluation methodology based on dynamic Bayesian networks: A case study of a subsea pipe ram BOP system

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