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.
Software Reliability is a special topic of software engineering that deals with the finding of glitches during the software development. Effective analysis of the reliability helps to understand the quality of the software. It also helps to reveal the number of failures occurred in development phase which facilitates refinement of the failures in the developed software’s. If the failures are not minimized the number of reviews in the software development process increases which in turn increase the expenditure to develop the software. Every software organization aims at releasing the software in time and also it becomes a mandate to manage the software such that the time to release the software is optimized. It becomes a mandate for any organization to release software patches so as to minimize the errors after software release and thereby if the number of patches increases, the credibility of the software together with the storage area will be at stake. This article presents a novel case study wherein a procedural layout is presented such that the number of failures can be reduced instantaneously and the failures are identified at the early stage. The development procedure laid in this article helps to formulate a basis for the distinction between true failures and non-failures. The work is presented using benchmark datasets and the results showcase a better recognition rate and failure deduction rate.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.