Imperfect debugging study of SRGM with fault reduction factor and multiple change point

Jain, Madhu; Manjula, T.; Gulati, T. R.

doi:10.1504/ijmor.2014.059526

Cited by 18 publications

(3 citation statements)

References 30 publications

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“…In Kapur et al, models have been proposed based on change point. Models incorporating both change point and imperfect debugging have been proposed in previous studies . From the area of multi‐release, in Kapur et al, a multi‐release software reliability model has been proposed from the view of perfect debugging wherein it is considered that total faults in each release depend on all the previous releases.…”

Section: Literature Surveymentioning

confidence: 99%

“…Models incorporating both change point and imperfect debugging have been proposed in previous studies. 15,24,25 From the area of multi-release, in Kapur et al, 2 a multi-release software reliability model has been proposed from the view of perfect debugging wherein it is considered that total faults in each release depend on all the previous releases. Then, another multi-release model is presented in Kapur et al, 1 which has been developed from the perspective of imperfect debugging and error generation in view of the assumption that the cumulative faults in new release depends on the faults generated from just previous release and on the faults generated because of addition of new upgrades to the existing system.…”

Section: Literature Surveymentioning

confidence: 99%

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Generalized multi‐release modelling of software reliability growth models from the perspective of two types of imperfect debugging and change point

Saraf

Iqbal

2019

Quality & Reliability Eng

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Owing to release of software in multiple releases, code changes take place in software. Because of this added complexity in software, the testing team may be unable to correct the fault upon detection, leaving the actual fault to reside in the software, termed as imperfect debugging or there may be replacement of original fault by other fault, leading to error generation. Many other factors exist that affect the testing phase of software like strategies of testing, test cases, skill, efficiency, and learning of testing team. All these factors cannot be kept stable during the whole process of testing. They may change at any time moment causing the background processes to experience change, which is known as change‐point. Keeping all these critical testing environment factors under consideration, a new software reliability growth model has been proposed, which is derived from an non homogenous Poisson process (NHPP)based unified scheme for multi‐release two‐stage fault detection/observation and correction/removal software reliability models. The developed model is numerically illustrated on tandem data set for four releases.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Generalized multi‐release modelling of software reliability growth models from the perspective of two types of imperfect debugging and change point

Saraf

Iqbal

2019

Quality & Reliability Eng

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“…In addition to the types of perfect debugging model [5][6][7] and imperfect debugging model [8][9], SRGM also takes into account the testing resources and testing environment. The SRGM based on testingeffort (TE) and change-point (CP) has been successively proposed [10][11][12][13][14]. Various types of TE began to appear widely in SRGM, making the cost of integrating test resources into modeling become the norm.…”

Section: Introductionmentioning

confidence: 99%

A Crowdsourced Test Defect Number Prediction Model Based on Test Labor and Test Reports

2023

Teh. vjesn.

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Software reliability growth model is widely used in measurement, prediction and reliability assurance. The uncertainty of software potential defects and the unpredictability of the distributed crowdsourced test process make the crowdsourced test platform crave software reliability modeling techniques to predict the number of potential defects of the software, to evaluate the progress of the test task. This paper puts forward a crowdsourced test defect number prediction model (CTDNPM) that considers both the quantity of test labor and the number of test reports as two test cost elements. A new reliability modeling framework is established based on element correlation, and three existing test work functions are combined to solve the equations, to predict the number of potential defects and the cumulative number of defects detected. The experimental results of four groups of real crowdsourced test data sets show that CTDNPM can predict the number of defects. The error of defect number estimation in the model is less than 10%, which has important guiding significance for monitoring the progress of the test task in the actual crowdsourced test.

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Release Planning Analysis Through Testing Coverage and Fault Reduction Factor Based Models with Change Point Perspective

Neha

Kaur

Jindal

2021

Springer Series in Reliability Engineering

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Imperfect debugging study of SRGM with fault reduction factor and multiple change point

Cited by 18 publications

References 30 publications

Generalized multi‐release modelling of software reliability growth models from the perspective of two types of imperfect debugging and change point

Generalized multi‐release modelling of software reliability growth models from the perspective of two types of imperfect debugging and change point

A Crowdsourced Test Defect Number Prediction Model Based on Test Labor and Test Reports

Release Planning Analysis Through Testing Coverage and Fault Reduction Factor Based Models with Change Point Perspective

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