Software reliability analysis models

Ohba, Mitsuru

doi:10.1147/rd.284.0428

Cited by 502 publications

(224 citation statements)

References 7 publications

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“…The following table-4 presents the results of goodness-of-fit under R 2 comparison criteria .989 .989 .989 .989 [3] .919 .992 .992 .992 [4] .990 .990 .990 .990 [5] .934 .934 .934 .934 [6] .978 .977 .977 .978 [7] .987 .989 .989 .989…”

Section: Results and Comparative Analysismentioning

confidence: 99%

“…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%

“…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%

“…An insight into imperfect debugging can be found in Obha [3,7], Pham [8], Kapur and Younes [9], Shyur [10] and Chiu and Huang [11]. Other realistic assumptions for running environment, testing/debugging strategies and resource allocation can also affect the reliability growth as discussed in Chiu and Huang [11] and Shyur [10].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Results and Comparative Analysismentioning

confidence: 99%

Section: Parameter Estimationmentioning

confidence: 99%

Section: Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of Some Software Reliability Growth Models with Learning Effects

Iqbal¹

2016

IJMSC

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“…Removing all detected faults will presumably increase the reliability of the software. Ohba [10] conceived that there were two types of faults namely mutually independent and mutually dependent faults. M utually independent faults can be detected and corrected immediately.…”

Section: Introductionmentioning

confidence: 99%

Software Reliability Estimation using Yamada delayed S Shaped Model under Imperfect Debugging and Time Lag

Gupta¹,

Choudhary²,

Saxena³

2011

IJCA

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Software Reliability Theory

Lyu

2002

Encyclopedia of Software Engineering

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Software reliability modeling has, surprisingly to many, been around since the early 1970s with the pioneering works of Jelinski and Moranda, Shooman, and Coutinho. The theory behind software reliability is presented, and some of the major models that have appeared in the literature from both historical and applications perspectives are described. Emerging techniques for software reliability research field are also included. The following four key components in software reliability theory and modeling: historical background, theory, modeling, and emerging techniques are addressed. These items are discussed in a general way, rather than attempting to discuss a long list of details. 1.1.5. Time Reliability quantities are defined with respect to time, although it is possible to define them with respect to other bases such as program runs of number of transactions. We discuss the notation of time in the next session. 1.1.6. Failure Functions When a time basis is determined, failures can be expressed in several ways: the cumulative failure function, the failure intensity function, the failure rate function, and the meantime to failure function. The cumulative failure function (also called the mean-value function) denotes the expected cumulative failures associated with each point of time. The failure intensity function represents the rate of change of the cumulative failure function. The failure rate function (or called the rate of occurrence of failures) is defined as the probability that a failure per unit time occurs in the interval [t , t + Dt], given that a failure has not occurred before t. The mean time to failure (MTTF) function represents the expected time that the next failure will be observed. (MTTF is also known as MTBF, mean time between failures.) 1.1.7. Mean Time to Repair and Availability Another quantity related to time is mean time to repair (MTTR), which represents the expected time until a system will be repaired after a failure is observed. When the MTTF and MTTR for a system are measured, its availability can be obtained. Availability is the probability that a system is available when needed. Typically, it is measured by 1.1.8. Operational Profile The operational profile of a system is defined as the set of operations that the software can execute along with the probability with which they will occur. An operation is a group of runs that typically involve similar processing. SOFTWARE RELIABILITY ENGINEERING in this encyclopedia provides a detailed description on the structure, development, illustration, and project application of the operational profile. In general, the number of possible software operations is quite large. When it is not practical to determine all the operations and their probabilities in complete detail, operations based on grouping or partitioning of input states (or system states) into domains are determined. 1.2. The Advantages of Using Execution Time Three kinds of time are relevant to software reliability: execution time, calendar time, and clock time. The execution time f...

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Software reliability analysis models

Cited by 502 publications

References 7 publications

Analysis of Some Software Reliability Growth Models with Learning Effects

Analysis of Some Software Reliability Growth Models with Learning Effects

Software Reliability Estimation using Yamada delayed S Shaped Model under Imperfect Debugging and Time Lag

Software Reliability Theory

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