Abstract: Software Reliability is a specialized area of software engineering which deals with the identification of failures while developing the software. Effective analysis of the reliability helps to signify the number of failures occurred during the development phase. This in turn aid in the refinement of the failures occurred during the development of software. This paper identifies a novel assessment to detect and eliminate the actual software failures efficiently. The approach fits in an exponential log normal distribution of Generalized Gamma Mixture Model (GGMM). The approach estimates two parameters using the Maximum Likelihood Estimate (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. 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 novel software reliability growth model which is more effectual in the identification of the failures significantly and facilitate the present software organizations in the release of software free from bugs just in time.

Jagadeesh Medapati, Prof Anand Chandulal J and Prof Rajinikanth T V, “A Novel Assessment to Achieve Maximum Efficiency in Optimizing Software Failures” International Journal of Advanced Computer Science and Applications(IJACSA), 10(3), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100337

@article{Medapati2019,
title = {A Novel Assessment to Achieve Maximum Efficiency in Optimizing Software Failures},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100337},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100337},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {3},
author = {Jagadeesh Medapati and Prof Anand Chandulal J and Prof Rajinikanth T V}
}