An effective software reliability growth model

Haque, Md. Asraful; Ahmad, Nesar

doi:10.1080/09617353.2021.1921547

Cited by 16 publications

(3 citation statements)

References 32 publications

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“…The mean absolute error (MAE) measures the difference between the predicted number of failures and the observed value, considering the number of observations as the sum of absolute values [56]. Predictive ratio risk (PRR) is measured by dividing the distance from the predicted number of failures to the actual number of failures by the predicted value with respect to model estimation [57]. Predictive power (PP) is measured as the distance from the actual number of failures to the predicted number of failures divided by the actual value [58].…”

Section: Criteriamentioning

confidence: 99%

Deep-Learning Software Reliability Model Using SRGM as Activation Function

Kim,

Pham,

Chang

2023

Applied Sciences

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Software is widely used in various fields. There is no place where it is not used from the smallest part to the entire part. In particular, the tendency to rely on software is accelerating as the fields of artificial intelligence and big data become more widespread. Therefore, it is extremely important to evaluate the reliability of software because of the extensive damage that could occur if the software fails. Previously, software reliability models were developed based on mathematical and statistical grounds; however, immediate response was difficult. Therefore, in this study, a software reliability model was developed that depends on data using deep learning, and it was analyzed by replacing the activation function previously used in deep learning with the proposed software reliability model. Since the sigmoid function has a similar shape to the software reliability model, we utilized this to propose a deep learning software reliability model that replaces the activation function, the sigmoid function, with the software reliability function. Two datasets were compared and analyzed using 10 criteria, and the superiority of the proposed deep-learning software reliability model was proved. In addition, the results were compared by changing the parameters utilized in the proposed deep-learning software reliability model by −10%, −5%, 5%, and 10%, and it was found that the larger the parameters, the smaller the change.

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Section: Criteriamentioning

confidence: 99%

Deep-Learning Software Reliability Model Using SRGM as Activation Function

Kim,

Pham,

Chang

2023

Applied Sciences

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“…It is also crucial to evaluate the software's reliability before its release in order to avoid risk and maintenance costs. A software system's reliability refers to its ability to function for a fixed period of time under certain operational and environmental circumstances (Haque and Ahmad, 2021). As a result, there is a significant need for high-quality software.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Role of Testing Coverage in the Software Reliability Assessment

Kumar

Aggarwal

Gupta

2023

Int. j. math. eng. manag. sci.

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To assure the reliability and quality of the final product, testing is an essential and crucial part in the software development cycle. During this process, fault correction/detection activities are carried out to increase the reliability of the software. The non-homogeneous Poisson Process (NHPP) is the basis of the investigated software reliability growth models (SRGMs), which are based on the supposition that the number of faults found is affected by the amount of code covered during testing and that the amount of code covered during testing depends on the testing effort expended. This research takes into consideration several testing coverage functions: exponential, delayed S-shaped and logistic distributions, to propose three SRGMs that are based on testing efforts. For testing effort expenditure Weibull distribution has been employed. Two real failure datasets have been utilised to validate the proposed models, and their performance is evaluated using four goodness-of-fit metrics, including predictive ratio risk (PRR), coefficient of determination (R^2 ), predictive power (PP) and mean square error (MSE). Sensitivity analysis of cost requirement-based release time of software for exponential function has been done by using a genetic algorithm, which minimized the overall cost of the software subject to the requirement for reliability.

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“…Saxena et al [10] proposed a SRGM that assumes imperfect debugging by a two-step process that considers fault observation and fault removal. Haque [11] presented a new Software Reliability Growth Model (SRGM) with the structure of a Logistic Growth Model in which the fault detection rate increases as the test department's skill improves as the test progresses. Nafreen et al [12] developed a SRGM with a bathtub-shaped fault detection rate function and proposed the conditional maximization algorithms to fit the models.…”

Section: Introductionmentioning

confidence: 99%

Software Reliability Growth Model with Dependent Failures and Uncertain Operating Environments

2022

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Software is used in various industries, and its reliability has become an extremely important issue. For example, in the medical industry, software is used to provide medical services to underprivileged individuals. If a problem occurs with the software reliability, incorrect medical information may be provided. The software reliability is estimated using a software reliability growth model. However, most software reliability growth models assume that the failures are independent. In addition, it is assumed that the test and operating environments are the same. In this study, we propose a new software reliability growth model that assumes that software failures are dependent and uncertain operating environments. A comparison of the proposed model against existing NHPP SRMEs using actual datasets shows that the proposed model achieves the best fit.

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An effective software reliability growth model

Cited by 16 publications

References 32 publications

Deep-Learning Software Reliability Model Using SRGM as Activation Function

Deep-Learning Software Reliability Model Using SRGM as Activation Function

Modeling the Role of Testing Coverage in the Software Reliability Assessment

Software Reliability Growth Model with Dependent Failures and Uncertain Operating Environments

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