Analysis of Some Software Reliability Growth Models with Learning Effects

Iqbal, Javaid

doi:10.5815/ijmsc.2016.03.06

Cited by 7 publications

(9 citation statements)

References 21 publications

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“…𝒂 𝒊 (31) where R i represents the reliability for i release, M i represents mean value function for the model, and a i represents initial number of faults in the software.…”

Section: Evaluation Criteriamentioning

confidence: 99%

“…If the values of SSE and MSE are small, the predictive execution of the models is good [29]. Furthermore, the value of R2 has specific range from 0 to 1 [31]. If the value of R2 is large, the predictive execution of the models is good [29].…”

Section: Evaluation Criteriamentioning

confidence: 99%

“…For all GNOME dataset versions, every model has its mean value function with parameters needed to be estimated accurately to calculate software's reliability according to the equation number (31). LSE estimates these parameters for all models for all GNOME dataset versions as illustrated in Table 7, Table 8, and Table 9.…”

Section: A Mean Value Functions' Parameters Estimationsmentioning

confidence: 99%

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Reliability Assessment for Open-Source Software Using Deterministic and Probabilistic Models

Ramadan¹,

Harb²,

Mousa³

et al. 2022

IJITCS

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Nowadays, computer software plays a significant role in all fields of our life. Essentially open-source software provides economic benefits for software companies such that it allows building new software without the need to create it from scratch. Therefore, it is extremely used, and accordingly, open-source software’s quality is a critical issue and one of the top research directions in the literature. In the development cycles of the software, checking the software reliability is an important indicator to release software or not. The deterministic and probabilistic models are the two main categories of models used to assess software reliability. In this paper, we perform a comparative study between eight different software reliability models: two deterministic models, and six probabilistic models based on three different methodologies: perfect debugging, imperfect debugging, and Gompertz distribution. We evaluate the employed models using three versions of a standard open-source dataset which is GNU’s Not Unix Network Object Model Environment projects. We evaluate the employed models using four evaluation criteria: sum of square error, mean square error, R-square, and reliability. The experimental results showed that for the first version of the open-source dataset SRGM-4 based on imperfect debugging methodology achieved the best reliability result, and for the last two versions of the open-source dataset SRGM-6 based on Gompertz distribution methodology achieved the best reliability result in terms of sum of square error, mean square error, and R-square.

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“…𝒂 𝒊 (31) where R i represents the reliability for i release, M i represents mean value function for the model, and a i represents initial number of faults in the software.…”

Section: Evaluation Criteriamentioning

confidence: 99%

Section: Evaluation Criteriamentioning

confidence: 99%

Section: A Mean Value Functions' Parameters Estimationsmentioning

confidence: 99%

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Reliability Assessment for Open-Source Software Using Deterministic and Probabilistic Models

Ramadan¹,

Harb²,

Mousa³

et al. 2022

IJITCS

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“…IEEE defines software reliability as the probability that software will not fail within a specified time under specified conditions. The software reliability model refers to the reliability block diagram and mathematical model established to predict or estimate the reliability of the software [20][21].…”

Section: A Software Reliability and Modelmentioning

confidence: 99%

Software Defects Prediction Based on Hybrid Particle Swarm Optimization and Sparrow Search Algorithm

Yang

Wang

et al. 2021

IEEE Access

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Software defects reflect software quality, and software failures can be predicted through software reliability models. Aiming at the problem that the parameters of software reliability model are difficult to estimate, this paper used the hybrid algorithm for model parameter estimation to software defect prediction. As a typical swarm intelligence algorithm, PSO (Particle Swarm Optimization) has fast convergence but low solution accuracy. SSA (Sparrow Search Algorithm) not only has high search accuracy and fast convergence speed, but also has the advantages of good stability and strong robustness. Based on the characteristic that the fitness function proposed in this paper, this paper hybrid PSO and SSA to accelerate the convergence before the individual update of the SSA. At the same time, this paper also constructed a new fitness function based on the maximum likelihood estimation of the parameters, and used it for parameter initialization. Through the analysis of the experimental results of five sets of actual data sets, the optimization performance of the hybrid algorithm (SSA-PSO) was better than that of a single algorithm with higher convergence speed and more stable, accurate results. Moreover, with the support of the new fitness function, it effectively solved the problems of slow convergence speed and low accuracy of solution. The experimental results showed that the hybrid SSA-PSO could obtain the better solution, convergence speed and stability than single SSA and PSO in software defections estimation and prediction.

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“…White box and black box models are the two general classification of software reliability models (SRMs), the first one, take into consideration the software internal structure ,while the second one do not, SRGMs are considered as the black box models and are used for faults removal [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

Exploring Ant Lion Optimization Algorithm to Enhance the Choice of an Appropriate Software Reliability Growth Model

Abd-AlKareem¹,

Riyadh²

2018

IJCA

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Software reliability always related to software failures, in a past few decades a software reliability growth models (SRGMs) number have been developed to predict the software reliability under different environment, but there is no single model that best fits all the real life situations and so can be recommended universally. to predict the failures of software accurately, an appropriate and best model must be chosen, this will help to estimate the cost and delivery time of the project. In this paper, Ant Lion optimization (ALO) algorithm is proposed to optimize estimation of parameters and a choice procedure is used to select an appropriate model of the software reliability that best fit available dataset of an ongoing project of the software. Employing ALO algorithm for estimating the SRGM's parameters has provided more accurate prediction and enhance procedure of the selection, making a decision to select suitable SRGMs during the phases of the testing can be more easier to a developer of the software .The explored algorithm has been examined on various datasets of software projects and it has been noticed that this method is better than other methods proposed.

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Analysis of Some Software Reliability Growth Models with Learning Effects

Cited by 7 publications

References 21 publications

Reliability Assessment for Open-Source Software Using Deterministic and Probabilistic Models

Reliability Assessment for Open-Source Software Using Deterministic and Probabilistic Models

Software Defects Prediction Based on Hybrid Particle Swarm Optimization and Sparrow Search Algorithm

Exploring Ant Lion Optimization Algorithm to Enhance the Choice of an Appropriate Software Reliability Growth Model

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