A robust software reliability growth model for accurate detection of software failures
by Jagadeesh Medapati; Anand Chandulal Jasti; T.V. Rajinikanth
International Journal of Software Engineering, Technology and Applications (IJSETA), Vol. 2, No. 2, 2024

Abstract: This paper pinpoints to detect and eliminate the actual software failures efficiently. The approach fits in a particular case of generalised gamma mixture model (GGMM), namely exponential distribution. The approach estimates two parameters using maximum likelihood estimation (MLE). Standard evaluation metrics like mean square error (MSE), coefficient of determination (R²), sum of squares (SSE), and root means square error (RMSE) were calculated. For the justification of the model selection and goodness of fit various model selection frameworks like chi-square goodness of fit, Wald's test, Akaike information criteria (AIC), AICc and Schwarz criterion (SBC) were also estimated. The experimentation was carried out on five benchmark datasets which interpret the considered novel technique identifies the actual failures on par with the existing models. This paper presents a robust software reliability growth model which is more effectual in the identification of the failures. This helps the present software organisations in the release of bug-free software just in time.

Online publication date: Mon, 09-Sep-2024

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