Statistical Analysis of Time-Varying Characteristics of Testability Index Based on NHPP

Zhao, Zhiao; Zhang, Yong; Liu, Guanjun; Qiu, Jing

doi:10.1109/access.2017.2688698

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…Since the superposition model remains an NHPP (all intensity functions are NHPPs), the associated superposition model can be applicable [28]. Two important cases of NHPPs consist of the related intensity function following a power-law and log linear (exponential) function of time [29,30]. In such case, the equations become:…”

Section: Discussionmentioning

confidence: 99%

Cluster-based vulnerability assessment of operating systems and web browsers

et al. 2018

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Organizations face the issue of how to best allocate their security resources. Thus, they need an accurate method for assessing how many new vulnerabilities will be reported for the operating systems (OSs) and web browsers they use in a given time period. Our approach consists of clustering vulnerabilities by leveraging the text information within vulnerability records, and then simulating the mean value function of vulnerabilities by relaxing the monotonic intensity function assumption, which is prevalent among the studies that use software reliability models (SRMs) and nonhomogeneous Poisson process in modeling. We applied our approach to the vulnerabilities of four OSs (Windows, Mac, IOS, and Linux) and four web browsers (Internet Explorer, Safari, Firefox, and Chrome). Out of the total eight OSs and web browsers we analyzed using a power-law model issued from a family of SRMs, the model was statistically adequate for modeling in six cases. For these cases, in terms of estimation and forecasting capability, our results, compared to a power-law model without clustering, are more accurate in all cases but one.

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Section: Discussionmentioning

confidence: 99%

Cluster-based vulnerability assessment of operating systems and web browsers

et al. 2018

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“…where m dr (k) is the cumulative number of identified TDLs which can be got by Eq (24). Taking the partial derivatives of S yr with respect to A, b, a 0 as zeros, we can obtain the least squares estimators of A, b, a 0 .…”

Section: Estimation Of Tgm Parameters and Comparison Criterions mentioning

confidence: 99%

NHPP Testability Growth Model Considering Testability Growth Effort, Rectifying Delay, and Imperfect Correction

Zonghao³

et al. 2020

IEEE Access

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Over the last several years, many testability growth models (TGMs) have been developed to greatly facilitate engineers and managers in tracking and measuring the growth of testability as system is being improved. Most TGMs consider only one or two variation patterns of the aspects, such as the testability growth effort (TGE) in testability design limitation (TDL) identification, the rectifying delay and the new TDL introduction in TDL correction. However, the ignorance of such joint consideration may lead to a lower fitting ability to the fault detection/isolation data. Inspired by the counting idea of nonhomogeneous Poisson process (NHPP), a NHPP based testability growth model (TGM) considering the recurrence rate function (RRF) of TDL identification, TDL correction and new TDL introduction is proposed for the foundation of TGM. A real data set of a missile control system is used to validate the above TGMs in fitting ability, estimation accuracy and prediction capability. Results show that the bell-shaped curve can fit the identification process and rectifying delay process of TDL well, and the imperfect correction of TDL really exists in the testability growth test (TGT), and the inflected s-shaped and Gamma function based TGM gives good capacity to the real data set. INDEX TERMS TGM, NHPP, testability growth effort, rectifying delay, imperfect correction.

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“…[16][17][18][19] Test data before the final definition and acceptance are of high value and can be effectively utilized to optimize test planning, including test data during product development, virtual test data and historical test data. 13,20 To fully exploit prior data, the sequential posterior odd test (SPOT) method has been widely used, which integrates the advantages of Bayesian theory and sequential probability ratio test (SPRT) method. [17][18][19][21][22][23][24] The effective reduction of the sample size makes SPOT useful in engineering.…”

Section: Introductionmentioning

confidence: 99%

A censored sequential posterior odd test method in testability demonstration test planning

Zhang

Wang

Lin

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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Testability demonstration tests can effectively verify product capabilities of fault detection and isolation; however, they suffer from insufficient samples, long cycles and high costs due to destructiveness of fault injection tests, which leads to an increasing demand for small sample tests. The sequential posterior odd test can effectively reduce sample sizes, but test results can be random and the sample size may be large. In this article, a censored sequential posterior odd test method is proposed, which can control the incremental risk caused by forced censoring within a contracted range by risk splitting. The development process of the testability demonstration test based on the censored sequential posterior odd test is designed. The number of censored tests and the calculation method of the censored threshold are presented. The case application shows that with the same prior distribution and constraint parameters, the average sample size of the proposed method is smaller than that of the sequential posterior odd test and of the classical method considering risks for both producers and consumers. The presented method can further reduce the risk of misjudgment and the number of test samples, contributing to the reduction of the test cycle and costs.

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Statistical Analysis of Time-Varying Characteristics of Testability Index Based on NHPP

Cited by 9 publications

References 25 publications

Cluster-based vulnerability assessment of operating systems and web browsers

Cluster-based vulnerability assessment of operating systems and web browsers

NHPP Testability Growth Model Considering Testability Growth Effort, Rectifying Delay, and Imperfect Correction

A censored sequential posterior odd test method in testability demonstration test planning

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