Title: A robust software reliability growth model for accurate detection of software failures

Authors: Jagadeesh Medapati; Anand Chandulal Jasti; T.V. Rajinikanth

Addresses: Department of Computer Science and Engineering, GITAM Institute of Technology, Visakhapatnam, Andhra Pradesh, India ' Department of Computer Science and Engineering, GITAM Institute of Technology, Visakhapatnam, Andhra Pradesh, India ' Srinidhi Institute of Technology, Telangana, India

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.

Keywords: software reliability; error; reviews; generalised gamma mixture model; GGMM; benchmark datasets.

DOI: 10.1504/IJSETA.2024.141316

International Journal of Software Engineering, Technology and Applications, 2024 Vol.2 No.2, pp.81 - 97

Received: 13 Feb 2019
Accepted: 20 Nov 2019
Published online: 09 Sep 2024 *

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