An efficient parameter estimation of software reliability growth models using gravitational search algorithm

Choudhary, Ankur; Baghel, Anurag Singh; Sangwan, Om Prakash

doi:10.1007/s13198-016-0541-0

Cited by 12 publications

(9 citation statements)

References 23 publications

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“…It is based on cumulative faults over time. Few assumptions were made are given as follows [1,6,12] The mathematical representation of NHPP models at time t is represented by MVF m(t) as shown in Table 1.…”

Section: Related Workmentioning

confidence: 99%

“…Theil statistic [12,25] MSE and TS showed in Table 5. Maximum number of iterations is used as stopping criteria and value is set to 2000.…”

Section: Comparison Of Mse Values Of Proposed Technique With Lse Has mentioning

confidence: 99%

“…It is based on cumulative faults over time. Few assumptions were made are given as follows [1, 6, 12]: Failure detection at any time t follows non‐homogenous Poisson distribution. Failures are caused by faults remaining in the software. Detected and remained faults are proportional to each other at any point of time. Failure rate is uniformly affected by remaining faults. The repair of detected faults is immediate and does not introduce new faults. …”

Section: Related Workmentioning

confidence: 99%

“…[9] and claimed superiority of CS over PSO and ACO‐based estimation techniques. Kim et al [10] applied real valued‐GA (RGA) to estimate SRGM parameters and reported RGA performed better than other variant of GA. Choudhary et al [12] applied GSA‐based parameter estimation for SRGMs and reported its superiority over CS and GA.…”

Section: Related Workmentioning

confidence: 99%

“…Nature inspired approaches overcome these limitations for parameter estimation of SRGMs. Genetic algorithm (GA), real valued GA, ant colony optimisation (ACO), cuckoo search (CS) optimisation, particle swarm optimisation (PSO) and gravitational search algorithm (GSA) are applied for parameter estimation of SRGMs [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Efficient parameter estimation of software reliability growth models using harmony search

2017

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The primary challenge of software reliability growth model is to find the unknown model parameters that are used to validate on software failure dataset. Though, numerical estimation technique plays a vital role in parameter estimation of software reliability growth models, they are not optimal as they suffer from constraints sucha as sample size, biasing, and initialisation of parameters. In this study, a parameter estimation of software reliability growth model that utilises a variant of harmony search is proposed. Extensive experiments are conducted on seven different software datasets of varying complexity. A robust experimental setup is developed employing an orthogonal array and Taguchi method. Twofold performance comparisons are performed. First, the authors tested their proposed approach against Cuckoo search and numerical method (least square estimation) considering mean square error and Theil's statistics as a quality measure. Second, the authors applied statistical tests are performed that demonstrate the superiority of their approach over the others. The underlying motivation to conduct this study is to motivate researchers to utilise their approach for a better estimation of model parameters.

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Section: Related Workmentioning

confidence: 99%

“…Theil statistic [12,25] MSE and TS showed in Table 5. Maximum number of iterations is used as stopping criteria and value is set to 2000.…”

Section: Comparison Of Mse Values Of Proposed Technique With Lse Has mentioning

confidence: 99%