Analysis of Some Software Reliability Growth Models with Learning Effects

Iqbal, Javaid

doi:10.5815/ijmsc.2016.03.06

Cited by 6 publications

(8 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%

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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%

“…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%

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

Ramadan¹,

Harb²,

Mousa³

et al. 2022

IJITCS

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show abstract

“…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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“…When the embedded information of the version is not present, then consumers cannot detect the software running the version accurately. Hence, it becomes impossible to detect software intrusions (Iqbal, 2016). Manually delivering the version field is the best thing developers can perform while checking in the software files.…”

Section: Bug Localization By Version Tagsmentioning

confidence: 99%

Optimum bug fixing rate and bug fixing time detection by software reliability modelling

Narvaneni

Babu

2021

IJIUS

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PurposeSoftware reliability growth models (SRGMs) are used to assess and predict reliability of a software system. Many of these models are effective in predicting future failures unless the software evolves.Design/methodology/approachThis objective of this paper is to identify the best path for rectifying the BFT (bug fixing time) and BFR (bug fixing rate). Moreover, the flexible software project has been examined while materializing the BFR. To enhance the BFR, the traceability of bug is lessened by the version tag virtue in every software deliverable component. The release time of software build is optimized with the utilization of mathematical optimization mechanisms like ‘software reliability growth’ and ‘non-homogeneous Poisson process methods.’FindingsIn current market scenario, this is most essential. The automation and variation of build is also resolved in this contribution. Here, the software, which is developed, is free from the bugs or defects and enhances the quality of software by increasing the BFR.Originality/valueIn current market scenario, this is most essential. The automation and variation of build is also resolved in this contribution. Here, the software, which is developed, is free from the bugs or defects and enhances the quality of software by increasing the BFR.

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Analysis of Some Software Reliability Growth Models with Learning Effects

Cited by 6 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

Optimum bug fixing rate and bug fixing time detection by software reliability modelling

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