Software failure prediction based on a Markov Bayesian network model

Bai, Chenggang; Hu, Qingpei; Xie, Min; Ng, Szu Hui

doi:10.1016/j.jss.2004.02.028

Cited by 70 publications

(21 citation statements)

References 31 publications

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“…It was also shown that the neural network architecture has a great impact on the performance of the network. According to Bai et al (2005) Bayesian networks show a strong ability to adapt in problems involving complex variant factors. They developed a software prediction model based on Markov Bayesian networks, and a method to solve the network model was proposed.…”

Section: Literature Surveymentioning

confidence: 99%

Software reliability prediction by soft computing techniques

Kiran

Ravi

2008

Journal of Systems and Software

120

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Section: Literature Surveymentioning

confidence: 99%

Software reliability prediction by soft computing techniques

Kiran

Ravi

2008

Journal of Systems and Software

120

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“…Pham and Zhang [18] Failure data of Tandem Software [5] Bai, Hu, Xie and Ng [19] Failure Data of Space program [6] Pham [20] Failure data of real time control system [7] Jeske and Zhang [21] Failure Data of wireless data service system…”

Section: Parameter Estimationmentioning

confidence: 99%

Analysis of Some Software Reliability Growth Models with Learning Effects

Iqbal¹

2016

IJMSC

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A newly developed software system before its deployment is subjected to vigorous testing so as to minimize the probability of occurrence of failure very soon. Software solutions for safety critical and mission-critical application areas need a much focused level of testing. The testing process is basically carried out to build confidence in the software for its use in real world applications. Thus, reliability of systems is always a matter of concern for us. As we keep on performing the error detection and correction process on our software, the reliability of the system grows. In order to model this growth in the system reliability, many formulations in Software Reliability Growth Models (SRGMs) have been proposed including some based on NonHomogeneous Poisson Process (NHPP). The role of human learning and experiential pattern gains are being studied and incorporated in such models. The realistic assumptions about human learning behavior and experiential gains of new skill-sets for better detection and correction of faults on software are being incorporated and studied in such models. In this paper, a detailed analysis of some select SRGMs with learning effects is presented based on use of seven data sets. The estimation of parameters and comparative analysis based on goodness of fit using seven data sets are presented. Moreover, model comparisons on the basis of total defects predicted by the select models are also tabulated.Index Terms: Software Reliability, Software Reliability Growth Model (SRGM), Non-Homogeneous Poisson Process (NHPP), Learning effect, two-type learning effect.

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“…Regarding software failure prediction, a number of models have been proposed [1][2][3][4]. In particular, the popular models are based on classic probability theory [1][2][3], and there do exist some other models which improves these popular models.…”

Section: Introductionmentioning

confidence: 99%

“…The neural network cannot process effectively with absent data of some input nodes. In this situation, Bai et al proposed a Markov Bayesian network approach for software failure prediction [4]. In this model, the restrictive assumptions are released because the rationality and accuracy of these assumptions are hard to verify.…”

Section: Introductionmentioning

confidence: 99%