Software reliability growth modeling with dynamic faults and release time optimization using GA and MAUT

Pachauri, Bhoopendra; Kumar, Ajay; Dhar, Joydip

doi:10.1016/j.amc.2014.05.107

Cited by 16 publications

(10 citation statements)

References 39 publications

(38 reference statements)

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“…We also note that previous papers have examined the problem of optimal release time . For instance, Rafi et al studied an SRGM and analyzed the optimal release policy by incorporating a generalized modified Weibull testing effort function.…”

Section: Introductionmentioning

confidence: 91%

Impacts of networking effects on software reliability growth processes: A multi‐attribute utility theory approach

Wen

2019

Quality & Reliability Eng

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Software reliability growth models, which are based on nonhomogeneous Poisson processes, are widely adopted tools when describing the stochastic failure behavior and measuring the reliability growth in software systems. Faults in the systems, which eventually cause the failures, are usually connected with each other in complicated ways. Considering a group of networked faults, we raise a new model to examine the reliability of software systems and assess the model's performance from real-world data sets. Our numerical studies show that the new model, capturing networking effects among faults, well fits the failure data. We also formally study the optimal software release policy using the multi-attribute utility theory (MAUT), considering both the reliability attribute and the cost attribute. We find that, if the networking effects among different layers of faults were ignored by the software testing team, the best time to release the software package to the market would be much later while the utility reaches its maximum. Sensitivity analysis is further delivered. KEYWORDS multi-attribute utility theory, networking effects, nonhomogeneous poisson processes, software release policy, software reliability growth model 1952

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Section: Introductionmentioning

confidence: 91%

Impacts of networking effects on software reliability growth processes: A multi‐attribute utility theory approach

Wen

2019

Quality & Reliability Eng

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“…The proposed model is motivated by the work of Kapur et al [11] and Pachauri et al [12]. In this section, a Software Reliability Growth Model based on Non Homogenous Poisson Process (NHPP) is developed by incorporating two things, one is the testing effort and second one is dynamic fault.…”

Section: Model Demonstrationmentioning

confidence: 99%

“…Pachauri et al [12] proposed Software reliability growth model with dynamic faults and optimal release time. Quadri et al [13] used generalized exponential curve as testing-effort function in Software Reliability Growth Model.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the work of Kapur et al [11], Pachauri et al [12] the focus is to develop a more efficient Software Reliability Growth Model in debugging environment using Generalized Modified Weibull Distribution.…”

Section: Introductionmentioning

confidence: 99%

“…The present study considered a growth model incorporating test effort and dynamic faults. A number of Software Reliability Growth Models have been constructed with or without testing effort [1][2][3][4][5][6][7][8][9][10][11][12]. Kapur et al [11] proposed Software reliability growth model with testing effort dependent learning function.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Testing Effort Dependent Software Reliability Growth Model with Dynamic Faults for Debugging Process

Dar¹,

Gowsami²,

Chaturvedi³

2015

IJCA

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In present era people depend on both hardware and software system. As software system is engrafted in every aspect of computer system, the desired quality of software is an essential concern for many critical system. From last few decades, many software reliability growth models were developed to analyze the growth of reliability. For improving the quality of software, SRGM plays an essential role. The present study proposed a Software Reliability Growth Model with testing effort and dynamic fault. The parameters involved in the proposed model are estimated using least square estimation. The performance of the proposed model is validated using Mean Square Error (MSE), Akaike Information Criterion (AIC) and R Squared Error (R 2 ). A proposed Model is compared with existing models reported in literature, and it has been observed that proposed model performed better.

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Modeling Multi‐Release Open Source Software Reliability Growth Process with Generalized Modified Weibull Distribution

Pradhan¹,

Kumar²,

Dhar³

2022

Evolving Software Processes

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Software reliability growth modeling with dynamic faults and release time optimization using GA and MAUT

Cited by 16 publications

References 39 publications

Impacts of networking effects on software reliability growth processes: A multi‐attribute utility theory approach

Impacts of networking effects on software reliability growth processes: A multi‐attribute utility theory approach

Testing Effort Dependent Software Reliability Growth Model with Dynamic Faults for Debugging Process

Modeling Multi‐Release Open Source Software Reliability Growth Process with Generalized Modified Weibull Distribution

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