Time-Dependent Error-Detection Rate Model for Software Reliability and Other Performance Measures

Goel, Amrit L.; Okumoto, Kazuhira

doi:10.1109/tr.1979.5220566

Cited by 1,588 publications

(701 citation statements)

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“…In other way if we consider the general function of a Poisson process (not necessarily linear) m(t) as its mean value function then probability equation of a such a process is [12] …”

Section: B Sequential Test For Software Reliability Growth Modelsmentioning

confidence: 99%

An Analogy of Burr Type III and Pareto Type II using SPRT

2017

IJIET

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Abstract-Software reliability is viewed as one of the key attributes of software quality, an important quality metric for any developed software. The aim is to statistically measure, ensure and predict the reliability of the software product and is accessed by the use of any statistical model, whose unknown parameters are estimated from the available software failure data. Researchers developed Sequential Probability Ratio Test (SPRT) to judge upon reliable/unreliable software based software reliability techniques. An analogy between Burr Type III and Pareto Type II with SPRT mechanism based on time domain data of Non Homogenous Poisson Process (NHPP) is presented here.

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“…In other way if we consider the general function of a Poisson process (not necessarily linear) m(t) as its mean value function then probability equation of a such a process is [12] …”

Section: B Sequential Test For Software Reliability Growth Modelsmentioning

confidence: 99%

An Analogy of Burr Type III and Pareto Type II using SPRT

2017

IJIET

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“…The following [11] a=135.965, α =.138, η=1.000E-4 2TL1 [5] a=135.965, α =49.216, η1 =.003, η2 =1.000E-4 2TL2 [6] a=135.974, α =2.611, η1 =2.566, η2 =.001, τ =6.562 Comparison under dataset [2] Model Parameters G-O [1] a= 218.159, b=.041 Chiu [11] a=215.706, α =.042, η=.001 2TL1 [5] a=215.706, α =56.69, η1 =.001, η2 =.001 2TL2 [6] a=210.134, α =.094, η1 =.442, η2 =1.000E-5, τ =7.007E-5 Comparison under dataset [3] Model Parameters G-O [1] a= 33.6, b=.063 Chiu [11] a=24.821, α =.024, η=.343 2TL1 [5] a=24.821, α =.056, η1 =.424, η2 =.343 2TL2 [6] a=24.821, α =.217, η1 =.658, η2 =.343, τ =.119 Comparison under dataset [4] Model Parameters G-O [1] a= 133.761, b=.015 Chiu [11] a=133.496, α =.146, η=.001 2TL1 [5] a=133.496, α =153.843, η1 =.001, η2 =.001 2TL2 [6] a=133.496, α =.002, η1 =909.569, η2 =.001, τ =1.748 Comparison under dataset [5] Model Parameters G-O [1] a= 18.257, b=.397 Chiu [11] a=18.254, α =.397, η=.001 2TL1 [5] a=18.254, α =.049, η1 =8.151, η2 =.001 2TL2 [6] a=18.257, α =23.658, η1 =.665, η2 =1.000E-5, τ =15.341 Comparison under dataset [6] Model Parameters G-O [1] a= 124.44, b=.051 Chiu [11] a=124.171, α =.051, η=.001 2TL1 [5] a=124.171, α =.001, η1 =88.486, η2 =.001 2TL2 [6] a=124.437, α =.163, η1 =5.874, η2 =1.000E-5, τ =.909 Comparison under dataset [7] Model Parameters G-O [1] a= 23.092, b=.559 Chiu [11] a=22.252, α =.493, η=.332 2TL1 [5] a=22.252, α =.211, η1 =.2.338, η2 =.332 2TL2 [6] a=22.252, α =9.048, η1 =.195, η2 =.332, τ =1.272…”

Section: Parameter Estimationmentioning

confidence: 99%

“…Goel and Okumoto in [1] proposed an exponential SRGM. Yamada and Ohba in [2] proposed delayed S-shaped SRGM while Ohba in [3] proposed inflection S-shaped SRGM.…”

Section: Introductionmentioning

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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“…An estimated 95% confidence interval is computed, with lower bound the 0.025 percentile and upper bound the 0.975 percentile of the bootstrap sample. The expected number of FMs that activate between day 600 and day 849 is computed using (29) and (31). The same bootstrap replications are used to estimate parameters for all the models.…”

Section: Estimation: One Type Of Fm; All Fms Present Initially; Each mentioning

confidence: 99%