Assessing Software Reliability Based on NHPP Using SPC

Kim, Hee-cheul; Sebuk-gu, Seonghwan-eup; Chungcheongnam-do, Cheonan-si

doi:10.14257/ijseia.2013.7.6.06

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…In the first part of the paper, the control chart for estimated number of failures in successive failure time intervals against the serial order of the failure interval is developed with the associated control lines and central line at same serial point on that of Kim(2013) [2].…”

Section: Discussionmentioning

confidence: 99%

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Time Control Charts Through NHPP Based on Dagum Distribution

2018

ijaa

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Abstract. Statistical process control is a method of monitoring product in its development process using statistical techniques with the presumption that the products produced under identical process condition shall not always be alike with respect to some quality characteristic(s). However, if the observed variations are within the tolerable limits statistical process control (SPC) methods would pass them for acceptance.This philosophy is adopted to decide the reliability and quality of a product by defining some quality measures and proposing a probability model for the quality measurements. The well known Dagum distribution (DD) is considered to propose a product reliability based on non-homogenous Poisson process (NHPP). Its mean value function is taken as a quality characteristic and SPC limits for it are developed. These control limits are exemplified to a live failure data to detect the out of control signals for the quality of the product based on the failure data and compared with Exponential distribution(ED).

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Section: Discussionmentioning

confidence: 99%

Section: Distribution and Its Propertiesmentioning

confidence: 99%

Time Control Charts Through NHPP Based on Dagum Distribution

2018

ijaa

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“…In this paper, the control chart for estimated number of failures in consecutive failure time intervals against the serial order of the failure interval is developed with the associated control lines and central line at same serial point on that of Kim(2013) [6]. However the basic model for our chart is a more generalized one allowing IFR as well as CFR failure behavior, whereas that of Kim(2013) [6] relates to only CFR model. Hence our conclusion is in a broader domain of models and hence can be generalized version than that of Kim(2013) [6].…”

Section: Discussionmentioning

confidence: 99%

“…However the basic model for our chart is a more generalized one allowing IFR as well as CFR failure behavior, whereas that of Kim(2013) [6] relates to only CFR model. Hence our conclusion is in a broader domain of models and hence can be generalized version than that of Kim(2013) [6].…”

Section: Discussionmentioning

confidence: 99%

“…The time control chart based on the mean value function corresponding to inter failure time together with three parallel lines to the horizontal axis at t L , t C and t U for the data of Kim(2013) [6] is given below. 21 116 2 21 12 77 22 149 3 32 13 78 23 156 4 36 14 87 24 247 5 43 15 91 25 249 6 45 16 92 26 250 7 50 17 95 27 337 8 58 18 98 28 384 9 63 19 104 29 396 10 70 20 105 30 405 Table 3.…”

Section: Monitoring the Time Between Failures For Mean Value Functionmentioning

confidence: 99%

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Software Quality Control Truncated Time with Linear Failure Rate Distribution based on NHPP

Rao¹,

Rao²

2016

IJCA

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Statistical process control is a method of monitoring product in its development process using statistical techniques with the presumption that the products produced under identical process condition shall not always be alike with respect to some quality characteristic(s). However, if the observed variations are with in the tolerable limits statistical process control (SPC) methods would pass them for acceptance. This philosophy is adopted to decide the reliability and quality of a developed software by defining some quality measures and proposing a probability model for the quality measurements. The well known linear failure rate distribution(LFRD) is considered to propose a software reliability based on non-homogenous Poisson process (NHPP). Its mean value function is taken as a quality characteristic and SPC limits for it are developed. These control limits are exemplified to a live failure data to detect the out of control signals for the quality of the software based on the software failure data.

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A Comprehensive Evaluation for Burr-Type NHPP-based Software Reliability Models

Okamura

2021

2021 8th International Conference on Dependable Systems and Their Applications (DSA)

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Assessing Software Reliability Based on NHPP Using SPC

Abstract: Abstract

Cited by 5 publications

References 14 publications

Time Control Charts Through NHPP Based on Dagum Distribution

Time Control Charts Through NHPP Based on Dagum Distribution

Software Quality Control Truncated Time with Linear Failure Rate Distribution based on NHPP

A Comprehensive Evaluation for Burr-Type NHPP-based Software Reliability Models

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